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$viewFile = '/home/summarytime/summurai.com/app/View/Article/landing.ctp' $dataForView = array( 'data' => array( 'MyItem' => array( 'id' => '7190', 'user_master_id' => '188', 'guid' => null, 'posted_by' => '332', 'voice_by' => '1561', 'post_market_id' => '5399', 'image_url' => 'http://www.bbvadata.com/wp-content/uploads/2016/12/discover-weekly-ml.jpg', 'title' => 'Experience Design in the Machine Learning Era', 'other_title' => '', 'description' => 'Traditionally the experience of a digital service follows pre-defined user journeys with clear states and actions. Until recently, it has been the designer’s job to create these linear workflows and transform them into understandable and unobtrusive experiences. This is the story of how that practice is about to change. Over the last 6 months, I have been working in a rather unique position at BBVA Data & Analytics, a center of excellence in financial data analysis. My job is to make the design of user experiences reach a new frontier with the emergence of machine learning techniques. My responsibility — among other things — is to bring a holistic experience design to teams of data scientists and make it an essential part of the lifecycle of algorithmic solutions (e.g. predictive models, recommender systems). In parallel, I perform creative and strategic reviews of experiences that design teams produce (e.g. online banking, online shopping, smart decision making) to steer their evolution into a future of “artificial intelligenceâ€. Practically, I boost the partnerships between teams of designers and data scientists to envision desirable and feasible experiences powered by data and algorithms. Nowadays, the design of many digital services does not only rely on data manipulation and information design but also on systems that learn from their users. If you would open the hood of these systems, you would see that behavioral data (e.g. human interactions, transactions with systems) is fed as context to algorithms that generates knowledge. An interface communicates that knowledge to enrich an experience. Ideally, that experience seeks explicit user actions or implicit sensor events to create a feedback loop that will feed the algorithm with learning material. Discovery Weekly is Spotify’s automated music recommendations “data engine†that brings two hours of custom-made music recommendations, tailored specifically to each Spotify user every Monday. The Discover Weekly’s recommender system leverages the millions playlists that Spotify users create. It gives extra weight to the company’s own experts playlists and those with more followers. The algorithm attempts to augment a person’s listening habits with those with similar tastes. It does it in three main tasks: A typical Discover Weekly playlist recommends 30 songs, a big enough set to discover music that matches with a personal taste among other false positives. That experience provokes the curation of thousands of new playlists that are fed back into the algorithm a week after to generate new recommendations. These feedback loop mechanisms typically offer ways to personalize, optimize or automate existing services. They also create opportunities to design new experiences based on recommendations, predictions or contextualization. At BBVA Data & Analytics I came up with a first non-comprehensive list: We have seen that recommender systems help discover the known unknown or even the unknown unknowns. For instance, Spotify helps discover music through a personalized experience defined on the match between an individual listening behavior and the listening behavior of hundreds of thousands of other individuals. That type of experience has at least three major design challenges. First, recommenders systems have a tendency to create a “filter bubble†that limits suggestions (e.g. products, restaurants, news items, people to connect with) to a world that is strictly linked to a profile built on past behaviors. In response, data scientists must sometimes tweak their algorithms to be less accurate and add a dose of randomness to the suggestions. Second, it is also good design practice to let an open door for users to reshape aspects of their profile that influence the discovery. I would call that feature “profile detoxâ€. Amazon for example allows users to remove items that might negatively influence the recommendations. Imagine the customers purchase gifts for others and those gifts are not necessarily material for future personalized recommendations. Finally, organizations that rely on subjective recommendation like Spotify now enlist humans to give more subjectivity and diversity to the suggested music. This approach of using humans to clean datasets or mitigate the limitations of machine learning algorithm is commonly called “Human Computation†or “Interactive Machine Learningâ€. Data and algorithms also provide means to personalize decision making. For instance at BBVA Data & Analytics we developed advanced techniques to advise BBVA customers on their finance. For example, we consider the temporal evolution of account balances to segment savings behaviors. With that technique we are able to personalize investment opportunities according to each customer’s capacity to save money. This type of algorithms that leads to decision-making needs to learn to be more precise, simply because they often rely on datasets that only give a perspective of reality. In the case of financial advisory, a customer could operate multiple accounts with other banks preventing a clear view on on saving behaviors. It proved a good design practice to let users tell implicitly or explicitly about poor information. It is the data scientist’s responsibility to express the types of feedback that enrich their models and the designer’s job to find ways to make it part of the experience. Traditionally the design of computer programs follows a binary logic with an explicit finite set of concrete and predictable states translated into a workflow. Machine learning algorithms change this with their inherent fuzzy logic. They are designed to look for patterns within a set of sample behaviors to probabilistically approximate the rules of these behaviors (see Machine Learning for Designers for a more detailed introduction to the topic). This approach comes with a certain degree imprecision and unpredictable behaviors. They often return some information on the precision of the information given. For example the booking platform Kayak predicts the evolution of prices according to the analysis of historical prices changes. Its “farecasting†algorithm is designed to return confidence on whether it is a favorable moment to purchase a ticket (see The Machine Learning Behind Farecast). A data scientist is naturally inclined to measure how accurately the algorithm predicts a value: “We predict this fare will be xâ€. That ‘prediction’ is in fact an information based on historical trends. Yet predicting is not the same as informing and a designer must consider how well such a prediction could support a user action: “Buy! this fare is likely to increaseâ€. The ‘likely’ with an overview of the price trend is an example of a “beautiful seam†in the user experience, a notion coined by Mark Weiser at the time of the Xerox Palo Alto Research Center and further developed by Chalmers and MacColl as seamful design: Seamful design is about exploiting failures and limitations to improve the experience. It is about improving the system allowing users to tell about poor recommendations. DJ Patil describes subtle techniques in Data Jujitsu. The ideal for an algorithm is to deliver high precision and recall scores. Unfortunately, precision and recall often work against each other. There is often a need to take design decisions with the trade-off between precision versus recall. For instance, in Spotify Discovery Weekly, a design decision had to be taken to define the size of playlists according to the performance of the recommender system. A large playlist highlights the confidence of Spotify to deliver a rather large inventory of 30 songs, a wide-enough set to increase the opportunities for users to stumble on perfect recommendations. Today, what we read online is based on our own behaviors and the behaviors of other users. Algorithms typically score the relevance of social and news content. The aim of these algorithms is to promote content for higher engagement or send notifications to create habits. Obviously these actions taken on our behalf are not necessarily for our own interest. In the attention economy, both designers and data scientists should learn from the anxieties, obsessions, phobias, stress and other mental burdens of the connected humans. Source: The Global Village and its Discomforts. Photo courtesy of Nicolas Nova. Arguably, we entered into the attention economy, and major online services are fighting to hook people, grap their attention for as long as possible. Their business is to keep users active as long and frequently as possible on their platforms. This leads to the development of sticky, needy experiences that often play with emotions like Fear of Missing Out (FoMO) or other obsessions to dope the user engagement. The actors of the attention economy use also techniques that promote addiction such as Variable Schedule Rewards. It is the exact same mechanisms as the ones used in slot machines. The resulting experience promotes the service’s interest (the casino) hooking people endlessly searching for the next reward. Our mobile phones have become those slot machines of notifications, alerts, messages, retweets, likes, that some of us check on an average 150 times per day if not more. Today designer can use data and algorithms to exploit cognitive vulnerabilities of people in their everyday lives. That new power raises the need for new design principles in the age of machine learning (see The ethics of good design: A principle for the connected age). There are opportunities to design a radically different experience than engagement. Indeed, an organization like a bank has the advantage of being a business that runs on data and does not need customers to spend the maximum amount of time with their services. Tristan Harris’ Time Well Spent movement is particularly inspiring in that sense. He promotes the type of experience that use data to be super-relevant or be silent. The type of technology to protect the user focus and to be respectful of people’s time. The Twitter “While you were away…†is a compelling example of that practice. Other services are good at suggesting moments to engage with them. Instead of measuring user retention, that type of experience focuses on how relevant the interactions are. Data scientist are good in detecting normal behavior and abnormal situations. At BBVA Data & Analytics we are working to promote a peace of mind to BBVA customers with mechanisms that gives a general awareness when things are fine and that trigger more detailed information on abnormal situations. More generally, we believe current generation of machine learning brings new powers to society, but also increases the responsibility of their creators. Algorithmic bias exists and may be inherent to the data sources. In consequence, there is a particular need to make algorithms more legible for people and auditable by regulators to understand their implications. Practically, this means knowledge that the an algorithm produces should safeguard the interest of their users and the results of the evaluation and the criteria used should be explained. In the previous section we have seen that the experiences powered by machine learning are not linear or based on static business and design rules. They evolves according to human behaviors with constantly updating models fed by streams of data. Each product or service becomes almost like a living, breathing thing. Or as people at Google would say: “It’s a different kind of engineeringâ€. I would argue that it is also a different kind of design. For instance, Amazon explains Echo’s braininess as a thing that “continually learns and adds more functionality over timeâ€. This description highlights the need to design the experience for systems to learn from human behavior. Consequently, beyond considering the first contact and the onboarding experience, that type of product or service requires considerations on their use after 1 hour, 1 day, 1 year, etc. If you look at the promotional video of the Edyn garden sensor you will notice the evolution of the experience from creating new habits for taking care of a garden to communicating the unknown unknowns about plants, to convey peace of mind on the key metrics, and to guarantee time well spent with some level of watering automation. That type of data product requires a responsible design that considers moments when things start to disappoint, embarrass, annoy or stop working or being useful. The design of the “offboarding experience†could become almost as important as the “onboarding experienceâ€. For instance, allegedly a third of the Fitbit users stop wearing the device within 6 months. What happens to these millions of abandoned connected objects? What happens to the data and intelligence on the individual they produced? What are the opportunities to use them in different experiences? Products characterized by an experience that evolves according to behavioral data that constantly feed algorithms (e.g. Fitbit) are living products that inevitably also have a tendency to die. Source: The Life and Death of Data Products. There are new ways to imagine the relation after a digital break-up with a product. Digital services work on an increasingly vast ecosystem of things and channels but user data have a tendency to be more centralized. Think about the notion of portable reputation that allows people to use a service based on the relation measured with another service. Looking a bit further into the near future, the recent breakthrough in Natural Language Processing, Knowledge Representation, Voice Recognition and Nature Language Production could create more subtle and stronger relations with machines. In a few iterations, Amazon Echo might start to be much more nurturing. A potential evolution that anthropologist Genevieve Bell foresees a shift from human-computer interactions to human-computer relationships in The next wave of AI is rooted in human culture and history: “So the frame there is not about recommendations, which is where much of AI is now, but is actually about nurture and care. If those become the buzzwords, then you sit in this very interesting moment of being able to pivot from talking about human-computer interactions to human-computer relationships.â€â€Šâ€” Genevieve Bell In this section we have seen that algorithms are getting closer to our everyday lives and that data provide a context for an evolving relationship. The implications of that evolution require most intense collaboration between design and data science. My experience so far envisioning experiences with data and algorithms shows that it is a different practice from current human-centered design. At BBVA Data & Analytics, the role of data scientists has been elevated from reactive model and A/B test developers to proactive partners who think about the implications of their work. Our singular data science teams breaks into sub-teams that partner more directly with engineers, designers, and product managers. At the moment of shaping an experience, we exploit thick data, the qualitative information that provides insights on people’s lives (see Why Big Data Needs Thick Data), big data from the aggregated behavioral data of millions of people and the small data that each individual generates. Classically, designers focus on defining the experience of the service, feature or product. They nest the concept within the larger ecosystem that relates to it. Data scientists develop the algorithms that will support that experience and measure it with A/B testing. The first few weeks in my role at BBVA Data & Analytics, I found designers and data scientists often stuck in deadlocked exchanges that typically sounded like this: The main issue was the lack of shared understanding of each other’s practice and objectives. For instance, designers transform a context into a form of experience. Data scientists transform a context with data and models into knowledge. Designers often adopt a path that adapts to a changing context and new appreciations. Data scientists employ processes similar to humber-center design but are more mechanical and less organic. They strictly follow the scientific methods with its cyclical processes of constant refinement. A properly formulated research question helps define the hypothesis and the types of models to develop in the prototyping phase. The models are the algorithms that get evaluated before they are deployed to production into what we call at BBVA Data & Analytics a “data engineâ€. Whenever the experience supported by the “data engine†does not perform as expected, the problem needs to be reformulated to continue the cyclical process of constant refinement. The scientific method is similar to any design approach that forms and makes new appreciations as new iterations are necessary. Yet, it is not an open-ended process. It has a clear start and end but no definite timeline. Data scientist Neal Lathia argues that “cross-disciplinary work is hard, until you’re speaking the same languageâ€. Additionally, I believe designers and data scientists must immerse themselves in the other’s practice to build a common rhythm. So far, I codified several important touchpoints for designers and data scientists to produce a meaningful user experience powered by algorithms. They must: This intertwined collaboration illustrates a new type of design that I am trying to articulate. In a recent article Harry West CEO at frog suggested the term ‘design of system behavior’: “Human-centered design has expanded from the design of objects (industrial design) to the design of experiences (adding interaction design, visual design, and the design of spaces) and the next step will be the design of system behavior: the design of the algorithms that determine the behavior of automated or intelligent systemsâ€â€Šâ€” Harry West So far I have argued that “living experiences†emerge at the crossroad of data science and design. An indispensable first step is for designers and data scientists is to establish a tangible vision and its outcomes (e.g. experience, solution, priorities, goals, scope and awareness of feasibility). Airbnb Director of Product Jonathan Golden calls that a vision-driven product management approach: “Your company vision is what you want the world to look like in five-plus years — outcomes are the team mandates that will help you get there.†— Jonathan Golden However, that conceptualization phase requires that visions live not just as flat perfect things for board room PowerPoint. Therefore, one of my approaches is to engage the design/science partnership to produce Design Fictions. It has similarities with Amazon’s Working Backward’ process as described by Werner Vogels: “You start with your customer and work your way backwards until you get to the minimum set of technology requirements to satisfy what you try to achieve. The goal is to drive simplicity through a continuous, explicit customer focus.â€â€Šâ€” Werner Vogels Thinking by doing with Design Fiction creates potential futures of a technology to clarify the present. Schema inspired by the Futures Cones and Matt Jones: Jumping to the End — Practical Design Fiction. Design Fiction aims at making tangible the evolution of technologies, the language used to describe them, the rituals, the magic moments, the frustrations, and why not the “offboarding experience”. It helps the different stakeholders of a project to engage with essential questions to understand what the desired experience means and why the team should build it. What are the implications of purchasing that next generation Garden Sensor? What can you do with it? What aren’t you allowed to do? What won’t you do anymore? How does a human interact with that technology the first time, and then routinely after a month, one year or more? Creative and tangible answers to these questions can come to life before a project even starts with the creation of fictional customer reviews, user manual, press release, ads. That material is a way to bring the future to present or as we say at the Near Future Laboratory: “The Design Fictions act as a totem for discussion and evaluation of changes that could bend visions of the desirable and planning of what is necessary.†At BBVA Data & Analytics, this means that I gather data scientists and designers with the objective of creating a tangible vision of their research agenda. First, we first map the ongoing lines of investigations. Then we project their evolution into 2 or 3 iterations wondering: What would the potential resulting technology look like? Where could it be used? Who would use it and for what type of experience? Each participant uses the template of a fictional ad to tell stories with practical answers to these questions. Together we group them into future concepts. We collect all the material and promote the most promising concepts. After that, we share these results internally in series of paper and video advertisements that describe the main features, attributes, characteristics of the experience from our point of view (the feasible) and the user’s point of view (the desirable). This type of fictional material allows both designers and data scientists to feel and get a practical understanding of the technology and its experience. The results help build credibility, enlist support, counter skepticism, create momentum and share a common vision. Finally, the feedback of people with different perspectives allows to anticipate opportunities and challenges. With the advance of machine learning and “artificial intelligence†(AI), it became the responsibility of both designers and data scientists to understand how to shape experiences that improve lives. Or as Greg Borenstein argues in Power to the People: How One Unknown Group of Researchers Holds the Key to Using AI to Solve Real Human Problems: “What’s needed for AI’s wide adoption is an understanding of how to build interfaces that put the power of these systems in the hands of their human users.†— Greg Borenstein That type of design of system behavior represents a future in the tight partnership between design and data science. So far in that journey of creating meaningful experiences in the machine learning era, I can articulate the following characteristics: This is an extended transcript of a talk I gave at the Design Wednesdays event at the BBVA Innovation Center in Madrid on September 21, 2016. Many thanks to the BBVA Design team for their invitation and the quality of the organization!', 'summary' => '<p>This article by author Fabien Girardin discusses the duties in his position as a designer at "B.B.V.A. Data and Analytics Company. He assists in the design of systems to provide enhanced user interactions of various software products by using comprehensive machine learning methods with teams of fellow data scientists with the company. What data scientist and designers in his company like others are doing in the new are of machine learning is upgrading their design systems into systems that uses machine learning by use of artificial intelligence, to have software in items like online shopping, banking and decision recommending predict the user's product preferences, selections, and purchase decisions based on data the machines learn from the users. In this article, Mr. Girardin’s goal is to improve on user experience with design systems his company is responsible for. He believes the new practice of providing the enhanced user satisfaction will beneficial for the customer and the company for 3 reasons. The first is that it will create new types of customer interactions. This means as new technology and software comes out, it will be the"systems designers plus data scientists" jobs to work together to create ways for the machine to learn from the user so that data can be gathered to better understand user preferences for example, movie and music genres. In other words, the more the customer uses the machine more the machine learns. The second reason is the evolution of the relationship between the user and the machine. In this article, Alexa is used as an example, and in its marketing it is used to do everything from turning on the lights to ordering pizza. It has a human and machine helper relationship and this is done by machine learning as the user continues to use the Alexa device. The third reason is in the teamwork relationship between the designers and data scientists." In the field of machine learning, "systems designers plus data scientists will be need to work together to create the system. The designers to create the user friendly interface and the data scientists enable the system to learn from the data it collects. 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=> '7190', 'user_master_id' => '188', 'guid' => null, 'posted_by' => '332', 'voice_by' => '1561', 'post_market_id' => '5399', 'image_url' => 'http://www.bbvadata.com/wp-content/uploads/2016/12/discover-weekly-ml.jpg', 'title' => 'Experience Design in the Machine Learning Era', 'other_title' => '', 'description' => 'Traditionally the experience of a digital service follows pre-defined user journeys with clear states and actions. Until recently, it has been the designer’s job to create these linear workflows and transform them into understandable and unobtrusive experiences. This is the story of how that practice is about to change. Over the last 6 months, I have been working in a rather unique position at BBVA Data & Analytics, a center of excellence in financial data analysis. My job is to make the design of user experiences reach a new frontier with the emergence of machine learning techniques. My responsibility — among other things — is to bring a holistic experience design to teams of data scientists and make it an essential part of the lifecycle of algorithmic solutions (e.g. predictive models, recommender systems). In parallel, I perform creative and strategic reviews of experiences that design teams produce (e.g. online banking, online shopping, smart decision making) to steer their evolution into a future of “artificial intelligenceâ€. Practically, I boost the partnerships between teams of designers and data scientists to envision desirable and feasible experiences powered by data and algorithms. Nowadays, the design of many digital services does not only rely on data manipulation and information design but also on systems that learn from their users. If you would open the hood of these systems, you would see that behavioral data (e.g. human interactions, transactions with systems) is fed as context to algorithms that generates knowledge. An interface communicates that knowledge to enrich an experience. Ideally, that experience seeks explicit user actions or implicit sensor events to create a feedback loop that will feed the algorithm with learning material. Discovery Weekly is Spotify’s automated music recommendations “data engine†that brings two hours of custom-made music recommendations, tailored specifically to each Spotify user every Monday. The Discover Weekly’s recommender system leverages the millions playlists that Spotify users create. It gives extra weight to the company’s own experts playlists and those with more followers. The algorithm attempts to augment a person’s listening habits with those with similar tastes. It does it in three main tasks: A typical Discover Weekly playlist recommends 30 songs, a big enough set to discover music that matches with a personal taste among other false positives. That experience provokes the curation of thousands of new playlists that are fed back into the algorithm a week after to generate new recommendations. These feedback loop mechanisms typically offer ways to personalize, optimize or automate existing services. They also create opportunities to design new experiences based on recommendations, predictions or contextualization. At BBVA Data & Analytics I came up with a first non-comprehensive list: We have seen that recommender systems help discover the known unknown or even the unknown unknowns. For instance, Spotify helps discover music through a personalized experience defined on the match between an individual listening behavior and the listening behavior of hundreds of thousands of other individuals. That type of experience has at least three major design challenges. First, recommenders systems have a tendency to create a “filter bubble†that limits suggestions (e.g. products, restaurants, news items, people to connect with) to a world that is strictly linked to a profile built on past behaviors. In response, data scientists must sometimes tweak their algorithms to be less accurate and add a dose of randomness to the suggestions. Second, it is also good design practice to let an open door for users to reshape aspects of their profile that influence the discovery. I would call that feature “profile detoxâ€. Amazon for example allows users to remove items that might negatively influence the recommendations. Imagine the customers purchase gifts for others and those gifts are not necessarily material for future personalized recommendations. Finally, organizations that rely on subjective recommendation like Spotify now enlist humans to give more subjectivity and diversity to the suggested music. This approach of using humans to clean datasets or mitigate the limitations of machine learning algorithm is commonly called “Human Computation†or “Interactive Machine Learningâ€. Data and algorithms also provide means to personalize decision making. For instance at BBVA Data & Analytics we developed advanced techniques to advise BBVA customers on their finance. For example, we consider the temporal evolution of account balances to segment savings behaviors. With that technique we are able to personalize investment opportunities according to each customer’s capacity to save money. This type of algorithms that leads to decision-making needs to learn to be more precise, simply because they often rely on datasets that only give a perspective of reality. In the case of financial advisory, a customer could operate multiple accounts with other banks preventing a clear view on on saving behaviors. It proved a good design practice to let users tell implicitly or explicitly about poor information. It is the data scientist’s responsibility to express the types of feedback that enrich their models and the designer’s job to find ways to make it part of the experience. Traditionally the design of computer programs follows a binary logic with an explicit finite set of concrete and predictable states translated into a workflow. Machine learning algorithms change this with their inherent fuzzy logic. They are designed to look for patterns within a set of sample behaviors to probabilistically approximate the rules of these behaviors (see Machine Learning for Designers for a more detailed introduction to the topic). This approach comes with a certain degree imprecision and unpredictable behaviors. They often return some information on the precision of the information given. For example the booking platform Kayak predicts the evolution of prices according to the analysis of historical prices changes. Its “farecasting†algorithm is designed to return confidence on whether it is a favorable moment to purchase a ticket (see The Machine Learning Behind Farecast). A data scientist is naturally inclined to measure how accurately the algorithm predicts a value: “We predict this fare will be xâ€. That ‘prediction’ is in fact an information based on historical trends. Yet predicting is not the same as informing and a designer must consider how well such a prediction could support a user action: “Buy! this fare is likely to increaseâ€. The ‘likely’ with an overview of the price trend is an example of a “beautiful seam†in the user experience, a notion coined by Mark Weiser at the time of the Xerox Palo Alto Research Center and further developed by Chalmers and MacColl as seamful design: Seamful design is about exploiting failures and limitations to improve the experience. It is about improving the system allowing users to tell about poor recommendations. DJ Patil describes subtle techniques in Data Jujitsu. The ideal for an algorithm is to deliver high precision and recall scores. Unfortunately, precision and recall often work against each other. There is often a need to take design decisions with the trade-off between precision versus recall. For instance, in Spotify Discovery Weekly, a design decision had to be taken to define the size of playlists according to the performance of the recommender system. A large playlist highlights the confidence of Spotify to deliver a rather large inventory of 30 songs, a wide-enough set to increase the opportunities for users to stumble on perfect recommendations. Today, what we read online is based on our own behaviors and the behaviors of other users. Algorithms typically score the relevance of social and news content. The aim of these algorithms is to promote content for higher engagement or send notifications to create habits. Obviously these actions taken on our behalf are not necessarily for our own interest. In the attention economy, both designers and data scientists should learn from the anxieties, obsessions, phobias, stress and other mental burdens of the connected humans. Source: The Global Village and its Discomforts. Photo courtesy of Nicolas Nova. Arguably, we entered into the attention economy, and major online services are fighting to hook people, grap their attention for as long as possible. Their business is to keep users active as long and frequently as possible on their platforms. This leads to the development of sticky, needy experiences that often play with emotions like Fear of Missing Out (FoMO) or other obsessions to dope the user engagement. The actors of the attention economy use also techniques that promote addiction such as Variable Schedule Rewards. It is the exact same mechanisms as the ones used in slot machines. The resulting experience promotes the service’s interest (the casino) hooking people endlessly searching for the next reward. Our mobile phones have become those slot machines of notifications, alerts, messages, retweets, likes, that some of us check on an average 150 times per day if not more. Today designer can use data and algorithms to exploit cognitive vulnerabilities of people in their everyday lives. That new power raises the need for new design principles in the age of machine learning (see The ethics of good design: A principle for the connected age). There are opportunities to design a radically different experience than engagement. Indeed, an organization like a bank has the advantage of being a business that runs on data and does not need customers to spend the maximum amount of time with their services. Tristan Harris’ Time Well Spent movement is particularly inspiring in that sense. He promotes the type of experience that use data to be super-relevant or be silent. The type of technology to protect the user focus and to be respectful of people’s time. The Twitter “While you were away…†is a compelling example of that practice. Other services are good at suggesting moments to engage with them. Instead of measuring user retention, that type of experience focuses on how relevant the interactions are. Data scientist are good in detecting normal behavior and abnormal situations. At BBVA Data & Analytics we are working to promote a peace of mind to BBVA customers with mechanisms that gives a general awareness when things are fine and that trigger more detailed information on abnormal situations. More generally, we believe current generation of machine learning brings new powers to society, but also increases the responsibility of their creators. Algorithmic bias exists and may be inherent to the data sources. In consequence, there is a particular need to make algorithms more legible for people and auditable by regulators to understand their implications. Practically, this means knowledge that the an algorithm produces should safeguard the interest of their users and the results of the evaluation and the criteria used should be explained. In the previous section we have seen that the experiences powered by machine learning are not linear or based on static business and design rules. They evolves according to human behaviors with constantly updating models fed by streams of data. Each product or service becomes almost like a living, breathing thing. Or as people at Google would say: “It’s a different kind of engineeringâ€. I would argue that it is also a different kind of design. For instance, Amazon explains Echo’s braininess as a thing that “continually learns and adds more functionality over timeâ€. This description highlights the need to design the experience for systems to learn from human behavior. Consequently, beyond considering the first contact and the onboarding experience, that type of product or service requires considerations on their use after 1 hour, 1 day, 1 year, etc. If you look at the promotional video of the Edyn garden sensor you will notice the evolution of the experience from creating new habits for taking care of a garden to communicating the unknown unknowns about plants, to convey peace of mind on the key metrics, and to guarantee time well spent with some level of watering automation. That type of data product requires a responsible design that considers moments when things start to disappoint, embarrass, annoy or stop working or being useful. The design of the “offboarding experience†could become almost as important as the “onboarding experienceâ€. For instance, allegedly a third of the Fitbit users stop wearing the device within 6 months. What happens to these millions of abandoned connected objects? What happens to the data and intelligence on the individual they produced? What are the opportunities to use them in different experiences? Products characterized by an experience that evolves according to behavioral data that constantly feed algorithms (e.g. Fitbit) are living products that inevitably also have a tendency to die. Source: The Life and Death of Data Products. There are new ways to imagine the relation after a digital break-up with a product. Digital services work on an increasingly vast ecosystem of things and channels but user data have a tendency to be more centralized. Think about the notion of portable reputation that allows people to use a service based on the relation measured with another service. Looking a bit further into the near future, the recent breakthrough in Natural Language Processing, Knowledge Representation, Voice Recognition and Nature Language Production could create more subtle and stronger relations with machines. In a few iterations, Amazon Echo might start to be much more nurturing. A potential evolution that anthropologist Genevieve Bell foresees a shift from human-computer interactions to human-computer relationships in The next wave of AI is rooted in human culture and history: “So the frame there is not about recommendations, which is where much of AI is now, but is actually about nurture and care. If those become the buzzwords, then you sit in this very interesting moment of being able to pivot from talking about human-computer interactions to human-computer relationships.â€â€Šâ€” Genevieve Bell In this section we have seen that algorithms are getting closer to our everyday lives and that data provide a context for an evolving relationship. The implications of that evolution require most intense collaboration between design and data science. My experience so far envisioning experiences with data and algorithms shows that it is a different practice from current human-centered design. At BBVA Data & Analytics, the role of data scientists has been elevated from reactive model and A/B test developers to proactive partners who think about the implications of their work. Our singular data science teams breaks into sub-teams that partner more directly with engineers, designers, and product managers. At the moment of shaping an experience, we exploit thick data, the qualitative information that provides insights on people’s lives (see Why Big Data Needs Thick Data), big data from the aggregated behavioral data of millions of people and the small data that each individual generates. Classically, designers focus on defining the experience of the service, feature or product. They nest the concept within the larger ecosystem that relates to it. Data scientists develop the algorithms that will support that experience and measure it with A/B testing. The first few weeks in my role at BBVA Data & Analytics, I found designers and data scientists often stuck in deadlocked exchanges that typically sounded like this: The main issue was the lack of shared understanding of each other’s practice and objectives. For instance, designers transform a context into a form of experience. Data scientists transform a context with data and models into knowledge. Designers often adopt a path that adapts to a changing context and new appreciations. Data scientists employ processes similar to humber-center design but are more mechanical and less organic. They strictly follow the scientific methods with its cyclical processes of constant refinement. A properly formulated research question helps define the hypothesis and the types of models to develop in the prototyping phase. The models are the algorithms that get evaluated before they are deployed to production into what we call at BBVA Data & Analytics a “data engineâ€. Whenever the experience supported by the “data engine†does not perform as expected, the problem needs to be reformulated to continue the cyclical process of constant refinement. The scientific method is similar to any design approach that forms and makes new appreciations as new iterations are necessary. Yet, it is not an open-ended process. It has a clear start and end but no definite timeline. Data scientist Neal Lathia argues that “cross-disciplinary work is hard, until you’re speaking the same languageâ€. Additionally, I believe designers and data scientists must immerse themselves in the other’s practice to build a common rhythm. So far, I codified several important touchpoints for designers and data scientists to produce a meaningful user experience powered by algorithms. They must: This intertwined collaboration illustrates a new type of design that I am trying to articulate. In a recent article Harry West CEO at frog suggested the term ‘design of system behavior’: “Human-centered design has expanded from the design of objects (industrial design) to the design of experiences (adding interaction design, visual design, and the design of spaces) and the next step will be the design of system behavior: the design of the algorithms that determine the behavior of automated or intelligent systemsâ€â€Šâ€” Harry West So far I have argued that “living experiences†emerge at the crossroad of data science and design. An indispensable first step is for designers and data scientists is to establish a tangible vision and its outcomes (e.g. experience, solution, priorities, goals, scope and awareness of feasibility). Airbnb Director of Product Jonathan Golden calls that a vision-driven product management approach: “Your company vision is what you want the world to look like in five-plus years — outcomes are the team mandates that will help you get there.†— Jonathan Golden However, that conceptualization phase requires that visions live not just as flat perfect things for board room PowerPoint. Therefore, one of my approaches is to engage the design/science partnership to produce Design Fictions. It has similarities with Amazon’s Working Backward’ process as described by Werner Vogels: “You start with your customer and work your way backwards until you get to the minimum set of technology requirements to satisfy what you try to achieve. The goal is to drive simplicity through a continuous, explicit customer focus.â€â€Šâ€” Werner Vogels Thinking by doing with Design Fiction creates potential futures of a technology to clarify the present. Schema inspired by the Futures Cones and Matt Jones: Jumping to the End — Practical Design Fiction. Design Fiction aims at making tangible the evolution of technologies, the language used to describe them, the rituals, the magic moments, the frustrations, and why not the “offboarding experience”. It helps the different stakeholders of a project to engage with essential questions to understand what the desired experience means and why the team should build it. What are the implications of purchasing that next generation Garden Sensor? What can you do with it? What aren’t you allowed to do? What won’t you do anymore? How does a human interact with that technology the first time, and then routinely after a month, one year or more? Creative and tangible answers to these questions can come to life before a project even starts with the creation of fictional customer reviews, user manual, press release, ads. That material is a way to bring the future to present or as we say at the Near Future Laboratory: “The Design Fictions act as a totem for discussion and evaluation of changes that could bend visions of the desirable and planning of what is necessary.†At BBVA Data & Analytics, this means that I gather data scientists and designers with the objective of creating a tangible vision of their research agenda. First, we first map the ongoing lines of investigations. Then we project their evolution into 2 or 3 iterations wondering: What would the potential resulting technology look like? Where could it be used? Who would use it and for what type of experience? Each participant uses the template of a fictional ad to tell stories with practical answers to these questions. Together we group them into future concepts. We collect all the material and promote the most promising concepts. After that, we share these results internally in series of paper and video advertisements that describe the main features, attributes, characteristics of the experience from our point of view (the feasible) and the user’s point of view (the desirable). This type of fictional material allows both designers and data scientists to feel and get a practical understanding of the technology and its experience. The results help build credibility, enlist support, counter skepticism, create momentum and share a common vision. Finally, the feedback of people with different perspectives allows to anticipate opportunities and challenges. With the advance of machine learning and “artificial intelligence†(AI), it became the responsibility of both designers and data scientists to understand how to shape experiences that improve lives. Or as Greg Borenstein argues in Power to the People: How One Unknown Group of Researchers Holds the Key to Using AI to Solve Real Human Problems: “What’s needed for AI’s wide adoption is an understanding of how to build interfaces that put the power of these systems in the hands of their human users.†— Greg Borenstein That type of design of system behavior represents a future in the tight partnership between design and data science. So far in that journey of creating meaningful experiences in the machine learning era, I can articulate the following characteristics: This is an extended transcript of a talk I gave at the Design Wednesdays event at the BBVA Innovation Center in Madrid on September 21, 2016. Many thanks to the BBVA Design team for their invitation and the quality of the organization!', 'summary' => '<p>This article by author Fabien Girardin discusses the duties in his position as a designer at "B.B.V.A. Data and Analytics Company. He assists in the design of systems to provide enhanced user interactions of various software products by using comprehensive machine learning methods with teams of fellow data scientists with the company. What data scientist and designers in his company like others are doing in the new are of machine learning is upgrading their design systems into systems that uses machine learning by use of artificial intelligence, to have software in items like online shopping, banking and decision recommending predict the user's product preferences, selections, and purchase decisions based on data the machines learn from the users. In this article, Mr. Girardin’s goal is to improve on user experience with design systems his company is responsible for. He believes the new practice of providing the enhanced user satisfaction will beneficial for the customer and the company for 3 reasons. The first is that it will create new types of customer interactions. This means as new technology and software comes out, it will be the"systems designers plus data scientists" jobs to work together to create ways for the machine to learn from the user so that data can be gathered to better understand user preferences for example, movie and music genres. In other words, the more the customer uses the machine more the machine learns. The second reason is the evolution of the relationship between the user and the machine. In this article, Alexa is used as an example, and in its marketing it is used to do everything from turning on the lights to ordering pizza. It has a human and machine helper relationship and this is done by machine learning as the user continues to use the Alexa device. The third reason is in the teamwork relationship between the designers and data scientists." In the field of machine learning, "systems designers plus data scientists will be need to work together to create the system. 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'scheduler_image' => '', 'scheduler_footer' => '', 'scheduler_footer_show' => '1', 'scheduler_reminder_sender_name' => '', 'scheduler_reminder_sender_mail' => '', 'scheduler_reminder_title' => '', 'scheduler_reminder_invite_message' => '', 'scheduler_status' => '0', 'is_coming_soon' => '0', 'is_single_summy' => '0', 'is_embed_summy' => '0', 'from_app' => '0', 'from_livedemo' => '0', 'from_podcast' => '0', 'block_editing' => '0', 'is_declined' => '0', 'is_blocked' => '0', 'is_deleted' => '0', 'created' => '2017-09-19 20:20:58', 'modified' => '2023-09-05 06:48:24' ), 'UserMaster' => array( 'password' => '*****', 'id' => '188', 'full_name' => 'Joy West', 'first_name' => '', 'last_name' => '', 'username' => '', 'email' => '[email protected]', 'gender' => '3', 'description' => '<p><span style="box-sizing: border-box; color: #494949; font-family: 'Gotham SSm', Helvetica, Arial, sans-serif; font-size: 13px; background-color: #fafafa;" data-story-id="story_5f02f4457344e4c28da759dfcbda4e23" data-timestamp="1479416503679" data-text="Michigan" data-userid="627848094442815488" data-orgid="627848094447009793">Michigan</span><img style="box-sizing: border-box; vertical-align: middle; border: 0px; color: #494949; font-family: 'Gotham SSm', Helvetica, Arial, sans-serif; font-size: 13px; background-color: #fafafa;" alt="" /><span style="background-color: #fafafa; color: #494949; font-family: 'Gotham SSm', Helvetica, Arial, sans-serif; font-size: 13px;">Michiga</span></p> <p><img style="box-sizing: border-box; vertical-align: middle; border: 0px; color: #494949; font-family: 'Gotham SSm', Helvetica, Arial, sans-serif; font-size: 13px; background-color: #fafafa;" alt="" /></p>', 'avatar_id' => '1', 'profile_picture' => '', 'profile_image' => null, 'address' => null, 'phone' => null, 'city_id' => 'Michigan', 'country_id' => 'United States', 'state' => null, 'postalcode' => null, 'latitude' => '', 'longitude' => '', 'phone_skip_count' => '0', 'isd_code' => '', 'website' => '', 'is_sms_active' => '1', 'is_email_active' => '1', 'available_credits' => '1', 'activation' => '', 'type' => '2', 'auto_approve' => '1', 'ip' => '', 'pass_code' => '1482468698585cad5ab8c57', 'company_id' => '0', 'stripe_customer_id' => '', 'is_company_user' => '0', 'change_password_count' => '0', 'is_vip' => false, 'time_zone' => 'GMT-5', 'show_on_sign_in' => '1', 'is_system_user' => '0', 'notifications_for_subcription' => '1', 'payment_platform_id' => '1', 'payment_model' => '0', 'monthly_rate' => '0', 'price_factor' => '1', 'voice_hero_price' => '0', 'monthly_free_credits' => '0', 'is_endless' => '0', 'rank_master_id' => '2', 'from_conference' => false, 'is_blogger' => false, 'sub_title' => '', 'cover_image' => '', 'curator_structure' => '', 'is_curator' => '0', 'form_curator' => false, 'curator_type' => false, 'curator_categories' => '', 'initial_cnt_followers' => '0', 'is_science' => false, 'from_client' => '0', 'is_auto_close_tab' => '1', 'property_category_id' => '0', 'blogger_id' => '0', 'from_blogger' => false, 'language_id' => '1', 'from_knowledge_pack' => '0', 'is_subscriber' => '0', 'is_archived' => '0', 'summurai_type' => '0', 'contact_url' => '', 'ad_cta' => '', 'ad_url' => '', 'ad_image' => '', 'show_in_summurai' => '1', 'is_creator' => '0', 'is_marketer' => '0', 'is_entrepreneur' => '0', 'is_aienthusiast' => '0', 'audio_purpose_onboarding' => '', 'content_type_onboarding' => '', 'audio_type_onboarding' => '', 'relevent_content_onboarding' => '', 'set_meeting_onboarding' => '', 'select_client' => '0', 'default_user' => '0', 'selected_brand_id' => '0', 'show_explainer' => '0', 'ordering' => '0', 'from_app' => '0', 'from_onboarding' => '0', 'onboarding_status' => '0', 'from_embed_playlist' => '0', 'status' => false, 'last_login' => '2018-03-13 19:27:15', 'created_by' => null, 'modified_by' => '0', 'is_blocked' => true, 'is_deleted' => false, 'created' => '2016-11-17 21:04:24', 'modified' => '2022-03-22 16:09:53' ), 'PostBy' => array( 'password' => '*****', 'id' => '332', 'full_name' => 'Shira Cinamon Lindenblat', 'first_name' => '', 'last_name' => '', 'username' => 'shiracinamon', 'email' => '[email protected]', 'gender' => '3', 'description' => '', 'avatar_id' => '16', 'profile_picture' => '', 'profile_image' => null, 'address' => null, 'phone' => '526066674', 'city_id' => null, 'country_id' => 'Israel', 'state' => null, 'postalcode' => null, 'latitude' => '', 'longitude' => '', 'phone_skip_count' => '0', 'isd_code' => '972', 'website' => '', 'is_sms_active' => '1', 'is_email_active' => '1', 'available_credits' => '22', 'activation' => '', 'type' => '1', 'auto_approve' => '0', 'ip' => '77.125.25.193', 'pass_code' => '', 'company_id' => '0', 'stripe_customer_id' => '', 'is_company_user' => '0', 'change_password_count' => '0', 'is_vip' => true, 'time_zone' => '', 'show_on_sign_in' => '1', 'is_system_user' => '0', 'notifications_for_subcription' => '1', 'payment_platform_id' => '1', 'payment_model' => '0', 'monthly_rate' => '0', 'price_factor' => '1', 'voice_hero_price' => '0', 'monthly_free_credits' => '0', 'is_endless' => '1', 'rank_master_id' => '1', 'from_conference' => false, 'is_blogger' => false, 'sub_title' => '', 'cover_image' => '', 'curator_structure' => '', 'is_curator' => '0', 'form_curator' => false, 'curator_type' => false, 'curator_categories' => '', 'initial_cnt_followers' => '0', 'is_science' => false, 'from_client' => '0', 'is_auto_close_tab' => '0', 'property_category_id' => '0', 'blogger_id' => '0', 'from_blogger' => false, 'language_id' => '1', 'from_knowledge_pack' => '0', 'is_subscriber' => '0', 'is_archived' => '0', 'summurai_type' => '0', 'contact_url' => '', 'ad_cta' => '', 'ad_url' => '', 'ad_image' => '', 'show_in_summurai' => '1', 'is_creator' => '0', 'is_marketer' => '0', 'is_entrepreneur' => '0', 'is_aienthusiast' => '0', 'audio_purpose_onboarding' => '', 'content_type_onboarding' => '', 'audio_type_onboarding' => '', 'relevent_content_onboarding' => '', 'set_meeting_onboarding' => '', 'select_client' => '0', 'default_user' => '0', 'selected_brand_id' => '0', 'show_explainer' => '0', 'ordering' => '0', 'from_app' => '0', 'from_onboarding' => '0', 'onboarding_status' => '0', 'from_embed_playlist' => '0', 'status' => false, 'last_login' => null, 'created_by' => null, 'modified_by' => '0', 'is_blocked' => false, 'is_deleted' => false, 'created' => '2017-03-08 05:41:52', 'modified' => '2022-03-22 16:09:53' ), 'VoiceBy' => array( 'password' => '*****', 'id' => '1561', 'full_name' => 'Ikwo Ibiam', 'first_name' => '', 'last_name' => '', 'username' => 'ikwo-ibiam', 'email' => '[email protected]', 'gender' => '3', 'description' => '', 'avatar_id' => '6', 'profile_picture' => '', 'profile_image' => null, 'address' => null, 'phone' => null, 'city_id' => '', 'country_id' => 'United States', 'state' => null, 'postalcode' => null, 'latitude' => '', 'longitude' => '', 'phone_skip_count' => '0', 'isd_code' => '', 'website' => '', 'is_sms_active' => '1', 'is_email_active' => 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=> '', 'image' => '1464677692_paint_palette.png', 'white_image' => '59f71af15e958_paint_palette.png', 'ordering' => '5', 'is_deleted' => '0', 'is_blocked' => '0', 'created' => '2015-11-16 13:16:06', 'modified' => '2024-01-03 22:56:04', 'created_by' => '0', 'modified_by' => '0' ), 'Client' => array( 'id' => null, 'client_secret' => null, 'parrent_id' => null, 'user_master_id' => null, 'client_name' => null, 'slug' => null, 'website' => null, 'quote' => null, 'image_url' => null, 'brand_color' => null, 'voice_file' => null, 'play_time' => null, 'direction' => null, 'client_type' => null, 'account_type' => null, 'brand_id' => null, 'image_social_url' => null, 'language_id' => null, 'brand_cat_type' => null, 'property_category_id' => null, 'secendary_color' => null, 'tag_manager' => null, 'google_pixel' => null, 'facebook_pixel' => null, 'select_client_id' => null, 'default_client_id' => null, 'curator_id' => null, 'summurai_id' => null, 'voice_hero_id' => null, 'from_summybox' => null, 'brand_type' => null, 'embed_border_color' => null, 'embed_background_color' => null, 'embed_input_color' => null, 'embed_primary_color' => null, 'embed_color_opecity' => null, 'embed_hover_color' => null, 'demo_image_name' => null, 'demo_image_url' => null, 'embed_width' => null, 'embed_height' => null, 'embed_top' => null, 'embed_left' => null, 'embed_player_title' => null, 'embed_player_title_size' => null, 'embed_mobile_link' => null, 'embed_mobile_text' => null, 'active_star' => null, 'board_sms_message' => null, 'summy_sms_message' => null, 'is_discover_content' => null, 'is_summyboards' => null, 'is_newsletter_player' => null, 'is_embedded_player' => null, 'is_full_summy_editor' => null, 'is_request_summy' => null, 'is_quick_add_summy' => null, 'is_send_to_summy_archive' => null, 'is_import_podcast' => null, 'is_playlist_report' => null, 'allow_premium_voice' => null, 'allow_export_playlist' => null, 'is_create_boards' => null, 'board_limit' => null, 'is_create_summy' => null, 'summy_limit' => null, 'brand_credit' => null, 'brand_credit_used' => null, 'default_page' => null, 'default_client_msg' => null, 'pseudo_header_color' => null, 'pseudo_main_color' => null, 'pseudo_color_opacity' => null, 'pseudo_language_id' => null, 'pseudo_feedback_show' => null, 'pseudo_brand_name_show' => null, 'pseudo_brand_link_show' => null, 'pseudo_brand_link_type' => null, 'pseudo_logo_type' => null, 'pseudo_top_logo' => null, 'pseudo_favicon' => null, 'show_pseudo_alt_footer' => null, 'pseudo_footer_color' => null, 'pseudo_footer_text_color' => null, 'pseudo_alt_footer_type' => null, 'pseudo_alt_footer_logo' => null, 'embedded_header_color' => null, 'embedded_main_color' => null, 'embedded_color_opacity' => null, 'embedded_language_id' => null, 'embedded_feedback_show' => null, 'embedded_brand_name_show' => null, 'embedded_brand_link_show' => null, 'embedded_brand_link_type' => null, 'embedded_logo_type' => null, 'embedded_top_logo' => null, 'embedded_favicon' => null, 'embed_playter_color' => null, 'embed_playter_secondary' => null, 'embed_playter_delay' => null, 'embed_playter_location' => null, 'embed_playter_allow_lead' => null, 'embed_playter_allow_sticky_bottom' => null, 'embed_playter_allow_sticky_bottom_mob' => null, 'embed_playter_hide_inline_player' => null, 'embed_playter_email_source' => null, 'embed_playter_email_name' => null, 'embed_playter_cta_text' => null, 'home_feature_section_title' => null, 'home_feature_title' => null, 'home_feature_text' => null, 'home_feature_image' => null, 'home_feature_url' => null, 'studio_promo_message' => null, 'is_set_expiration' => null, 'brand_expiration' => null, 'timezone' => null, 'from_onboarding' => null, 'from_app' => null, 'from_livedemo' => null, 'from_embed_playlist' => null, 'status' => null, 'is_blocked' => null, 'is_deleted' => null, 'created' => null, 'modified' => null ), 'Language' => array( 'id' => '1', 'title' => 'English', 'short_code' => 'en', 'direction' => '0', 'flag' => 'https://dojo.summurai.com/img/langflag/big-eng-flg.jpg', 'status' => true, 'is_blocked' => false, 'is_deleted' => false, 'created' => '2017-09-15 03:00:00', 'modified' => '2017-09-15 03:00:00' ) ) $summy_lang = array( 'Language' => array( 'id' => '1', 'title' => 'English', 'short_code' => 'en', 'direction' => '0', 'flag' => 'https://dojo.summurai.com/img/langflag/big-eng-flg.jpg', 'status' => true, 'is_blocked' => false, 'is_deleted' => false, 'created' => '2017-09-15 03:00:00', 'modified' => '2017-09-15 03:00:00' ) ) $brand_details = array() $keywords = 'data,BBVA Data,data scientists,design,experience,data scientist,good design practice,holistic experience design,data science,algorithms,Spotify Discovery Weekly,data engine,BBVA Design team,financial data analysis,machine learning,new design principles,behavioral data,data science teams,Big Data Needs,major design challenges,BBVA customers,Data scientist Neal,radically different experience,user experience,meaningful user 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Learning Era' $item_summary = 'This article by author Fabien Girardin discusses the duties in his position as a designer at "B.B.V.A. Data and Analytics Company. He assists in the design of systems to provide enhanced user interactions of various software products by using comprehensive machine learning methods with teams of fellow data scientists with the company. What data scientist and designers in his company like others are doing in the new are of machine learning is upgrading their design systems into systems that uses machine learning by use of artificial intelligence, to have software in items like online shopping, banking and decision recommending predict the user's product preferences, selections, and purchase decisions based on data the machines learn from the users. In this article, Mr. Girardin’s goal is to improve on user experience with design systems his company is responsible for. He believes the new practice of providing the enhanced user satisfaction will beneficial for the customer and the company for 3 reasons. The first is that it will create new types of customer interactions. This means as new technology and software comes out, it will be the"systems designers plus data scientists" jobs to work together to create ways for the machine to learn from the user so that data can be gathered to better understand user preferences for example, movie and music genres. In other words, the more the customer uses the machine more the machine learns. The second reason is the evolution of the relationship between the user and the machine. In this article, Alexa is used as an example, and in its marketing it is used to do everything from turning on the lights to ordering pizza. It has a human and machine helper relationship and this is done by machine learning as the user continues to use the Alexa device. The third reason is in the teamwork relationship between the designers and data scientists." In the field of machine learning, "systems designers plus data scientists will be need to work together to create the system. The designers to create the user friendly interface and the data scientists enable the system to learn from the data it collects. In conclusion, the subject of machine learning is clearly explained in this article.' $item_site_name = 'BBVA Data & Analytics' $voice_url = 'https://summarytime.com/uploads/voice_file/7190.MP3'
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Notice (8): Undefined index: Client [APP/View/Article/landing.ctp, line 97]style="display:none;left:100%;">Code Context<!---------------- Help Desk Popup ---------------------><div class="over_lay_sub over-lay" style="display: none;"></div><div class="assistance-popup-panel only-bg-shadow help_desk_pop <?php echo (($brand_details['Client']['pseudo_language_id']==2)?'rtl':'')?>" <?php echo (($brand_details['Client']['pseudo_language_id']==2)?'style="display:none;right:100%;"':'style="display:none;left:100%;"')?>>$viewFile = '/home/summarytime/summurai.com/app/View/Article/landing.ctp' $dataForView = array( 'data' => array( 'MyItem' => array( 'id' => '7190', 'user_master_id' => '188', 'guid' => null, 'posted_by' => '332', 'voice_by' => '1561', 'post_market_id' => '5399', 'image_url' => 'http://www.bbvadata.com/wp-content/uploads/2016/12/discover-weekly-ml.jpg', 'title' => 'Experience Design in the Machine Learning Era', 'other_title' => '', 'description' => 'Traditionally the experience of a digital service follows pre-defined user journeys with clear states and actions. Until recently, it has been the designer’s job to create these linear workflows and transform them into understandable and unobtrusive experiences. This is the story of how that practice is about to change. Over the last 6 months, I have been working in a rather unique position at BBVA Data & Analytics, a center of excellence in financial data analysis. My job is to make the design of user experiences reach a new frontier with the emergence of machine learning techniques. My responsibility — among other things — is to bring a holistic experience design to teams of data scientists and make it an essential part of the lifecycle of algorithmic solutions (e.g. predictive models, recommender systems). In parallel, I perform creative and strategic reviews of experiences that design teams produce (e.g. online banking, online shopping, smart decision making) to steer their evolution into a future of “artificial intelligenceâ€. Practically, I boost the partnerships between teams of designers and data scientists to envision desirable and feasible experiences powered by data and algorithms. Nowadays, the design of many digital services does not only rely on data manipulation and information design but also on systems that learn from their users. If you would open the hood of these systems, you would see that behavioral data (e.g. human interactions, transactions with systems) is fed as context to algorithms that generates knowledge. An interface communicates that knowledge to enrich an experience. Ideally, that experience seeks explicit user actions or implicit sensor events to create a feedback loop that will feed the algorithm with learning material. Discovery Weekly is Spotify’s automated music recommendations “data engine†that brings two hours of custom-made music recommendations, tailored specifically to each Spotify user every Monday. The Discover Weekly’s recommender system leverages the millions playlists that Spotify users create. It gives extra weight to the company’s own experts playlists and those with more followers. The algorithm attempts to augment a person’s listening habits with those with similar tastes. It does it in three main tasks: A typical Discover Weekly playlist recommends 30 songs, a big enough set to discover music that matches with a personal taste among other false positives. That experience provokes the curation of thousands of new playlists that are fed back into the algorithm a week after to generate new recommendations. These feedback loop mechanisms typically offer ways to personalize, optimize or automate existing services. They also create opportunities to design new experiences based on recommendations, predictions or contextualization. At BBVA Data & Analytics I came up with a first non-comprehensive list: We have seen that recommender systems help discover the known unknown or even the unknown unknowns. For instance, Spotify helps discover music through a personalized experience defined on the match between an individual listening behavior and the listening behavior of hundreds of thousands of other individuals. That type of experience has at least three major design challenges. First, recommenders systems have a tendency to create a “filter bubble†that limits suggestions (e.g. products, restaurants, news items, people to connect with) to a world that is strictly linked to a profile built on past behaviors. In response, data scientists must sometimes tweak their algorithms to be less accurate and add a dose of randomness to the suggestions. Second, it is also good design practice to let an open door for users to reshape aspects of their profile that influence the discovery. I would call that feature “profile detoxâ€. Amazon for example allows users to remove items that might negatively influence the recommendations. Imagine the customers purchase gifts for others and those gifts are not necessarily material for future personalized recommendations. Finally, organizations that rely on subjective recommendation like Spotify now enlist humans to give more subjectivity and diversity to the suggested music. This approach of using humans to clean datasets or mitigate the limitations of machine learning algorithm is commonly called “Human Computation†or “Interactive Machine Learningâ€. Data and algorithms also provide means to personalize decision making. For instance at BBVA Data & Analytics we developed advanced techniques to advise BBVA customers on their finance. For example, we consider the temporal evolution of account balances to segment savings behaviors. With that technique we are able to personalize investment opportunities according to each customer’s capacity to save money. This type of algorithms that leads to decision-making needs to learn to be more precise, simply because they often rely on datasets that only give a perspective of reality. In the case of financial advisory, a customer could operate multiple accounts with other banks preventing a clear view on on saving behaviors. It proved a good design practice to let users tell implicitly or explicitly about poor information. It is the data scientist’s responsibility to express the types of feedback that enrich their models and the designer’s job to find ways to make it part of the experience. Traditionally the design of computer programs follows a binary logic with an explicit finite set of concrete and predictable states translated into a workflow. Machine learning algorithms change this with their inherent fuzzy logic. They are designed to look for patterns within a set of sample behaviors to probabilistically approximate the rules of these behaviors (see Machine Learning for Designers for a more detailed introduction to the topic). This approach comes with a certain degree imprecision and unpredictable behaviors. They often return some information on the precision of the information given. For example the booking platform Kayak predicts the evolution of prices according to the analysis of historical prices changes. Its “farecasting†algorithm is designed to return confidence on whether it is a favorable moment to purchase a ticket (see The Machine Learning Behind Farecast). A data scientist is naturally inclined to measure how accurately the algorithm predicts a value: “We predict this fare will be xâ€. That ‘prediction’ is in fact an information based on historical trends. Yet predicting is not the same as informing and a designer must consider how well such a prediction could support a user action: “Buy! this fare is likely to increaseâ€. The ‘likely’ with an overview of the price trend is an example of a “beautiful seam†in the user experience, a notion coined by Mark Weiser at the time of the Xerox Palo Alto Research Center and further developed by Chalmers and MacColl as seamful design: Seamful design is about exploiting failures and limitations to improve the experience. It is about improving the system allowing users to tell about poor recommendations. DJ Patil describes subtle techniques in Data Jujitsu. The ideal for an algorithm is to deliver high precision and recall scores. Unfortunately, precision and recall often work against each other. There is often a need to take design decisions with the trade-off between precision versus recall. For instance, in Spotify Discovery Weekly, a design decision had to be taken to define the size of playlists according to the performance of the recommender system. A large playlist highlights the confidence of Spotify to deliver a rather large inventory of 30 songs, a wide-enough set to increase the opportunities for users to stumble on perfect recommendations. Today, what we read online is based on our own behaviors and the behaviors of other users. Algorithms typically score the relevance of social and news content. The aim of these algorithms is to promote content for higher engagement or send notifications to create habits. Obviously these actions taken on our behalf are not necessarily for our own interest. In the attention economy, both designers and data scientists should learn from the anxieties, obsessions, phobias, stress and other mental burdens of the connected humans. Source: The Global Village and its Discomforts. Photo courtesy of Nicolas Nova. Arguably, we entered into the attention economy, and major online services are fighting to hook people, grap their attention for as long as possible. Their business is to keep users active as long and frequently as possible on their platforms. This leads to the development of sticky, needy experiences that often play with emotions like Fear of Missing Out (FoMO) or other obsessions to dope the user engagement. The actors of the attention economy use also techniques that promote addiction such as Variable Schedule Rewards. It is the exact same mechanisms as the ones used in slot machines. The resulting experience promotes the service’s interest (the casino) hooking people endlessly searching for the next reward. Our mobile phones have become those slot machines of notifications, alerts, messages, retweets, likes, that some of us check on an average 150 times per day if not more. Today designer can use data and algorithms to exploit cognitive vulnerabilities of people in their everyday lives. That new power raises the need for new design principles in the age of machine learning (see The ethics of good design: A principle for the connected age). There are opportunities to design a radically different experience than engagement. Indeed, an organization like a bank has the advantage of being a business that runs on data and does not need customers to spend the maximum amount of time with their services. Tristan Harris’ Time Well Spent movement is particularly inspiring in that sense. He promotes the type of experience that use data to be super-relevant or be silent. The type of technology to protect the user focus and to be respectful of people’s time. The Twitter “While you were away…†is a compelling example of that practice. Other services are good at suggesting moments to engage with them. Instead of measuring user retention, that type of experience focuses on how relevant the interactions are. Data scientist are good in detecting normal behavior and abnormal situations. At BBVA Data & Analytics we are working to promote a peace of mind to BBVA customers with mechanisms that gives a general awareness when things are fine and that trigger more detailed information on abnormal situations. More generally, we believe current generation of machine learning brings new powers to society, but also increases the responsibility of their creators. Algorithmic bias exists and may be inherent to the data sources. In consequence, there is a particular need to make algorithms more legible for people and auditable by regulators to understand their implications. Practically, this means knowledge that the an algorithm produces should safeguard the interest of their users and the results of the evaluation and the criteria used should be explained. In the previous section we have seen that the experiences powered by machine learning are not linear or based on static business and design rules. They evolves according to human behaviors with constantly updating models fed by streams of data. Each product or service becomes almost like a living, breathing thing. Or as people at Google would say: “It’s a different kind of engineeringâ€. I would argue that it is also a different kind of design. For instance, Amazon explains Echo’s braininess as a thing that “continually learns and adds more functionality over timeâ€. This description highlights the need to design the experience for systems to learn from human behavior. Consequently, beyond considering the first contact and the onboarding experience, that type of product or service requires considerations on their use after 1 hour, 1 day, 1 year, etc. If you look at the promotional video of the Edyn garden sensor you will notice the evolution of the experience from creating new habits for taking care of a garden to communicating the unknown unknowns about plants, to convey peace of mind on the key metrics, and to guarantee time well spent with some level of watering automation. That type of data product requires a responsible design that considers moments when things start to disappoint, embarrass, annoy or stop working or being useful. The design of the “offboarding experience†could become almost as important as the “onboarding experienceâ€. For instance, allegedly a third of the Fitbit users stop wearing the device within 6 months. What happens to these millions of abandoned connected objects? What happens to the data and intelligence on the individual they produced? What are the opportunities to use them in different experiences? Products characterized by an experience that evolves according to behavioral data that constantly feed algorithms (e.g. Fitbit) are living products that inevitably also have a tendency to die. Source: The Life and Death of Data Products. There are new ways to imagine the relation after a digital break-up with a product. Digital services work on an increasingly vast ecosystem of things and channels but user data have a tendency to be more centralized. Think about the notion of portable reputation that allows people to use a service based on the relation measured with another service. Looking a bit further into the near future, the recent breakthrough in Natural Language Processing, Knowledge Representation, Voice Recognition and Nature Language Production could create more subtle and stronger relations with machines. In a few iterations, Amazon Echo might start to be much more nurturing. A potential evolution that anthropologist Genevieve Bell foresees a shift from human-computer interactions to human-computer relationships in The next wave of AI is rooted in human culture and history: “So the frame there is not about recommendations, which is where much of AI is now, but is actually about nurture and care. If those become the buzzwords, then you sit in this very interesting moment of being able to pivot from talking about human-computer interactions to human-computer relationships.â€â€Šâ€” Genevieve Bell In this section we have seen that algorithms are getting closer to our everyday lives and that data provide a context for an evolving relationship. The implications of that evolution require most intense collaboration between design and data science. My experience so far envisioning experiences with data and algorithms shows that it is a different practice from current human-centered design. At BBVA Data & Analytics, the role of data scientists has been elevated from reactive model and A/B test developers to proactive partners who think about the implications of their work. Our singular data science teams breaks into sub-teams that partner more directly with engineers, designers, and product managers. At the moment of shaping an experience, we exploit thick data, the qualitative information that provides insights on people’s lives (see Why Big Data Needs Thick Data), big data from the aggregated behavioral data of millions of people and the small data that each individual generates. Classically, designers focus on defining the experience of the service, feature or product. They nest the concept within the larger ecosystem that relates to it. Data scientists develop the algorithms that will support that experience and measure it with A/B testing. The first few weeks in my role at BBVA Data & Analytics, I found designers and data scientists often stuck in deadlocked exchanges that typically sounded like this: The main issue was the lack of shared understanding of each other’s practice and objectives. For instance, designers transform a context into a form of experience. Data scientists transform a context with data and models into knowledge. Designers often adopt a path that adapts to a changing context and new appreciations. Data scientists employ processes similar to humber-center design but are more mechanical and less organic. They strictly follow the scientific methods with its cyclical processes of constant refinement. A properly formulated research question helps define the hypothesis and the types of models to develop in the prototyping phase. The models are the algorithms that get evaluated before they are deployed to production into what we call at BBVA Data & Analytics a “data engineâ€. Whenever the experience supported by the “data engine†does not perform as expected, the problem needs to be reformulated to continue the cyclical process of constant refinement. The scientific method is similar to any design approach that forms and makes new appreciations as new iterations are necessary. Yet, it is not an open-ended process. It has a clear start and end but no definite timeline. Data scientist Neal Lathia argues that “cross-disciplinary work is hard, until you’re speaking the same languageâ€. Additionally, I believe designers and data scientists must immerse themselves in the other’s practice to build a common rhythm. So far, I codified several important touchpoints for designers and data scientists to produce a meaningful user experience powered by algorithms. They must: This intertwined collaboration illustrates a new type of design that I am trying to articulate. In a recent article Harry West CEO at frog suggested the term ‘design of system behavior’: “Human-centered design has expanded from the design of objects (industrial design) to the design of experiences (adding interaction design, visual design, and the design of spaces) and the next step will be the design of system behavior: the design of the algorithms that determine the behavior of automated or intelligent systemsâ€â€Šâ€” Harry West So far I have argued that “living experiences†emerge at the crossroad of data science and design. An indispensable first step is for designers and data scientists is to establish a tangible vision and its outcomes (e.g. experience, solution, priorities, goals, scope and awareness of feasibility). Airbnb Director of Product Jonathan Golden calls that a vision-driven product management approach: “Your company vision is what you want the world to look like in five-plus years — outcomes are the team mandates that will help you get there.†— Jonathan Golden However, that conceptualization phase requires that visions live not just as flat perfect things for board room PowerPoint. Therefore, one of my approaches is to engage the design/science partnership to produce Design Fictions. It has similarities with Amazon’s Working Backward’ process as described by Werner Vogels: “You start with your customer and work your way backwards until you get to the minimum set of technology requirements to satisfy what you try to achieve. The goal is to drive simplicity through a continuous, explicit customer focus.â€â€Šâ€” Werner Vogels Thinking by doing with Design Fiction creates potential futures of a technology to clarify the present. Schema inspired by the Futures Cones and Matt Jones: Jumping to the End — Practical Design Fiction. Design Fiction aims at making tangible the evolution of technologies, the language used to describe them, the rituals, the magic moments, the frustrations, and why not the “offboarding experience”. It helps the different stakeholders of a project to engage with essential questions to understand what the desired experience means and why the team should build it. What are the implications of purchasing that next generation Garden Sensor? What can you do with it? What aren’t you allowed to do? What won’t you do anymore? How does a human interact with that technology the first time, and then routinely after a month, one year or more? Creative and tangible answers to these questions can come to life before a project even starts with the creation of fictional customer reviews, user manual, press release, ads. That material is a way to bring the future to present or as we say at the Near Future Laboratory: “The Design Fictions act as a totem for discussion and evaluation of changes that could bend visions of the desirable and planning of what is necessary.†At BBVA Data & Analytics, this means that I gather data scientists and designers with the objective of creating a tangible vision of their research agenda. First, we first map the ongoing lines of investigations. Then we project their evolution into 2 or 3 iterations wondering: What would the potential resulting technology look like? Where could it be used? Who would use it and for what type of experience? Each participant uses the template of a fictional ad to tell stories with practical answers to these questions. Together we group them into future concepts. We collect all the material and promote the most promising concepts. After that, we share these results internally in series of paper and video advertisements that describe the main features, attributes, characteristics of the experience from our point of view (the feasible) and the user’s point of view (the desirable). This type of fictional material allows both designers and data scientists to feel and get a practical understanding of the technology and its experience. The results help build credibility, enlist support, counter skepticism, create momentum and share a common vision. Finally, the feedback of people with different perspectives allows to anticipate opportunities and challenges. With the advance of machine learning and “artificial intelligence†(AI), it became the responsibility of both designers and data scientists to understand how to shape experiences that improve lives. Or as Greg Borenstein argues in Power to the People: How One Unknown Group of Researchers Holds the Key to Using AI to Solve Real Human Problems: “What’s needed for AI’s wide adoption is an understanding of how to build interfaces that put the power of these systems in the hands of their human users.†— Greg Borenstein That type of design of system behavior represents a future in the tight partnership between design and data science. So far in that journey of creating meaningful experiences in the machine learning era, I can articulate the following characteristics: This is an extended transcript of a talk I gave at the Design Wednesdays event at the BBVA Innovation Center in Madrid on September 21, 2016. Many thanks to the BBVA Design team for their invitation and the quality of the organization!', 'summary' => '<p>This article by author Fabien Girardin discusses the duties in his position as a designer at "B.B.V.A. Data and Analytics Company. He assists in the design of systems to provide enhanced user interactions of various software products by using comprehensive machine learning methods with teams of fellow data scientists with the company. What data scientist and designers in his company like others are doing in the new are of machine learning is upgrading their design systems into systems that uses machine learning by use of artificial intelligence, to have software in items like online shopping, banking and decision recommending predict the user's product preferences, selections, and purchase decisions based on data the machines learn from the users. In this article, Mr. Girardin’s goal is to improve on user experience with design systems his company is responsible for. He believes the new practice of providing the enhanced user satisfaction will beneficial for the customer and the company for 3 reasons. The first is that it will create new types of customer interactions. This means as new technology and software comes out, it will be the"systems designers plus data scientists" jobs to work together to create ways for the machine to learn from the user so that data can be gathered to better understand user preferences for example, movie and music genres. In other words, the more the customer uses the machine more the machine learns. The second reason is the evolution of the relationship between the user and the machine. In this article, Alexa is used as an example, and in its marketing it is used to do everything from turning on the lights to ordering pizza. It has a human and machine helper relationship and this is done by machine learning as the user continues to use the Alexa device. The third reason is in the teamwork relationship between the designers and data scientists." In the field of machine learning, "systems designers plus data scientists will be need to work together to create the system. The designers to create the user friendly interface and the data scientists enable the system to learn from the data it collects. 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Until recently, it has been the designer’s job to create these linear workflows and transform them into understandable and unobtrusive experiences. This is the story of how that practice is about to change. Over the last 6 months, I have been working in a rather unique position at BBVA Data & Analytics, a center of excellence in financial data analysis. My job is to make the design of user experiences reach a new frontier with the emergence of machine learning techniques. My responsibility — among other things — is to bring a holistic experience design to teams of data scientists and make it an essential part of the lifecycle of algorithmic solutions (e.g. predictive models, recommender systems). In parallel, I perform creative and strategic reviews of experiences that design teams produce (e.g. online banking, online shopping, smart decision making) to steer their evolution into a future of “artificial intelligenceâ€. Practically, I boost the partnerships between teams of designers and data scientists to envision desirable and feasible experiences powered by data and algorithms. Nowadays, the design of many digital services does not only rely on data manipulation and information design but also on systems that learn from their users. If you would open the hood of these systems, you would see that behavioral data (e.g. human interactions, transactions with systems) is fed as context to algorithms that generates knowledge. An interface communicates that knowledge to enrich an experience. Ideally, that experience seeks explicit user actions or implicit sensor events to create a feedback loop that will feed the algorithm with learning material. Discovery Weekly is Spotify’s automated music recommendations “data engine†that brings two hours of custom-made music recommendations, tailored specifically to each Spotify user every Monday. The Discover Weekly’s recommender system leverages the millions playlists that Spotify users create. It gives extra weight to the company’s own experts playlists and those with more followers. The algorithm attempts to augment a person’s listening habits with those with similar tastes. It does it in three main tasks: A typical Discover Weekly playlist recommends 30 songs, a big enough set to discover music that matches with a personal taste among other false positives. That experience provokes the curation of thousands of new playlists that are fed back into the algorithm a week after to generate new recommendations. These feedback loop mechanisms typically offer ways to personalize, optimize or automate existing services. They also create opportunities to design new experiences based on recommendations, predictions or contextualization. At BBVA Data & Analytics I came up with a first non-comprehensive list: We have seen that recommender systems help discover the known unknown or even the unknown unknowns. For instance, Spotify helps discover music through a personalized experience defined on the match between an individual listening behavior and the listening behavior of hundreds of thousands of other individuals. That type of experience has at least three major design challenges. First, recommenders systems have a tendency to create a “filter bubble†that limits suggestions (e.g. products, restaurants, news items, people to connect with) to a world that is strictly linked to a profile built on past behaviors. In response, data scientists must sometimes tweak their algorithms to be less accurate and add a dose of randomness to the suggestions. Second, it is also good design practice to let an open door for users to reshape aspects of their profile that influence the discovery. I would call that feature “profile detoxâ€. Amazon for example allows users to remove items that might negatively influence the recommendations. Imagine the customers purchase gifts for others and those gifts are not necessarily material for future personalized recommendations. Finally, organizations that rely on subjective recommendation like Spotify now enlist humans to give more subjectivity and diversity to the suggested music. This approach of using humans to clean datasets or mitigate the limitations of machine learning algorithm is commonly called “Human Computation†or “Interactive Machine Learningâ€. Data and algorithms also provide means to personalize decision making. For instance at BBVA Data & Analytics we developed advanced techniques to advise BBVA customers on their finance. For example, we consider the temporal evolution of account balances to segment savings behaviors. With that technique we are able to personalize investment opportunities according to each customer’s capacity to save money. This type of algorithms that leads to decision-making needs to learn to be more precise, simply because they often rely on datasets that only give a perspective of reality. In the case of financial advisory, a customer could operate multiple accounts with other banks preventing a clear view on on saving behaviors. It proved a good design practice to let users tell implicitly or explicitly about poor information. It is the data scientist’s responsibility to express the types of feedback that enrich their models and the designer’s job to find ways to make it part of the experience. Traditionally the design of computer programs follows a binary logic with an explicit finite set of concrete and predictable states translated into a workflow. Machine learning algorithms change this with their inherent fuzzy logic. They are designed to look for patterns within a set of sample behaviors to probabilistically approximate the rules of these behaviors (see Machine Learning for Designers for a more detailed introduction to the topic). This approach comes with a certain degree imprecision and unpredictable behaviors. They often return some information on the precision of the information given. For example the booking platform Kayak predicts the evolution of prices according to the analysis of historical prices changes. Its “farecasting†algorithm is designed to return confidence on whether it is a favorable moment to purchase a ticket (see The Machine Learning Behind Farecast). A data scientist is naturally inclined to measure how accurately the algorithm predicts a value: “We predict this fare will be xâ€. That ‘prediction’ is in fact an information based on historical trends. Yet predicting is not the same as informing and a designer must consider how well such a prediction could support a user action: “Buy! this fare is likely to increaseâ€. The ‘likely’ with an overview of the price trend is an example of a “beautiful seam†in the user experience, a notion coined by Mark Weiser at the time of the Xerox Palo Alto Research Center and further developed by Chalmers and MacColl as seamful design: Seamful design is about exploiting failures and limitations to improve the experience. It is about improving the system allowing users to tell about poor recommendations. DJ Patil describes subtle techniques in Data Jujitsu. The ideal for an algorithm is to deliver high precision and recall scores. Unfortunately, precision and recall often work against each other. There is often a need to take design decisions with the trade-off between precision versus recall. For instance, in Spotify Discovery Weekly, a design decision had to be taken to define the size of playlists according to the performance of the recommender system. A large playlist highlights the confidence of Spotify to deliver a rather large inventory of 30 songs, a wide-enough set to increase the opportunities for users to stumble on perfect recommendations. Today, what we read online is based on our own behaviors and the behaviors of other users. Algorithms typically score the relevance of social and news content. The aim of these algorithms is to promote content for higher engagement or send notifications to create habits. Obviously these actions taken on our behalf are not necessarily for our own interest. In the attention economy, both designers and data scientists should learn from the anxieties, obsessions, phobias, stress and other mental burdens of the connected humans. Source: The Global Village and its Discomforts. Photo courtesy of Nicolas Nova. Arguably, we entered into the attention economy, and major online services are fighting to hook people, grap their attention for as long as possible. Their business is to keep users active as long and frequently as possible on their platforms. This leads to the development of sticky, needy experiences that often play with emotions like Fear of Missing Out (FoMO) or other obsessions to dope the user engagement. The actors of the attention economy use also techniques that promote addiction such as Variable Schedule Rewards. It is the exact same mechanisms as the ones used in slot machines. The resulting experience promotes the service’s interest (the casino) hooking people endlessly searching for the next reward. Our mobile phones have become those slot machines of notifications, alerts, messages, retweets, likes, that some of us check on an average 150 times per day if not more. Today designer can use data and algorithms to exploit cognitive vulnerabilities of people in their everyday lives. That new power raises the need for new design principles in the age of machine learning (see The ethics of good design: A principle for the connected age). There are opportunities to design a radically different experience than engagement. Indeed, an organization like a bank has the advantage of being a business that runs on data and does not need customers to spend the maximum amount of time with their services. Tristan Harris’ Time Well Spent movement is particularly inspiring in that sense. He promotes the type of experience that use data to be super-relevant or be silent. The type of technology to protect the user focus and to be respectful of people’s time. The Twitter “While you were away…†is a compelling example of that practice. Other services are good at suggesting moments to engage with them. Instead of measuring user retention, that type of experience focuses on how relevant the interactions are. Data scientist are good in detecting normal behavior and abnormal situations. At BBVA Data & Analytics we are working to promote a peace of mind to BBVA customers with mechanisms that gives a general awareness when things are fine and that trigger more detailed information on abnormal situations. More generally, we believe current generation of machine learning brings new powers to society, but also increases the responsibility of their creators. Algorithmic bias exists and may be inherent to the data sources. In consequence, there is a particular need to make algorithms more legible for people and auditable by regulators to understand their implications. Practically, this means knowledge that the an algorithm produces should safeguard the interest of their users and the results of the evaluation and the criteria used should be explained. In the previous section we have seen that the experiences powered by machine learning are not linear or based on static business and design rules. They evolves according to human behaviors with constantly updating models fed by streams of data. Each product or service becomes almost like a living, breathing thing. Or as people at Google would say: “It’s a different kind of engineeringâ€. I would argue that it is also a different kind of design. For instance, Amazon explains Echo’s braininess as a thing that “continually learns and adds more functionality over timeâ€. This description highlights the need to design the experience for systems to learn from human behavior. Consequently, beyond considering the first contact and the onboarding experience, that type of product or service requires considerations on their use after 1 hour, 1 day, 1 year, etc. If you look at the promotional video of the Edyn garden sensor you will notice the evolution of the experience from creating new habits for taking care of a garden to communicating the unknown unknowns about plants, to convey peace of mind on the key metrics, and to guarantee time well spent with some level of watering automation. That type of data product requires a responsible design that considers moments when things start to disappoint, embarrass, annoy or stop working or being useful. The design of the “offboarding experience†could become almost as important as the “onboarding experienceâ€. For instance, allegedly a third of the Fitbit users stop wearing the device within 6 months. What happens to these millions of abandoned connected objects? What happens to the data and intelligence on the individual they produced? What are the opportunities to use them in different experiences? Products characterized by an experience that evolves according to behavioral data that constantly feed algorithms (e.g. Fitbit) are living products that inevitably also have a tendency to die. Source: The Life and Death of Data Products. There are new ways to imagine the relation after a digital break-up with a product. Digital services work on an increasingly vast ecosystem of things and channels but user data have a tendency to be more centralized. Think about the notion of portable reputation that allows people to use a service based on the relation measured with another service. Looking a bit further into the near future, the recent breakthrough in Natural Language Processing, Knowledge Representation, Voice Recognition and Nature Language Production could create more subtle and stronger relations with machines. In a few iterations, Amazon Echo might start to be much more nurturing. A potential evolution that anthropologist Genevieve Bell foresees a shift from human-computer interactions to human-computer relationships in The next wave of AI is rooted in human culture and history: “So the frame there is not about recommendations, which is where much of AI is now, but is actually about nurture and care. If those become the buzzwords, then you sit in this very interesting moment of being able to pivot from talking about human-computer interactions to human-computer relationships.â€â€Šâ€” Genevieve Bell In this section we have seen that algorithms are getting closer to our everyday lives and that data provide a context for an evolving relationship. The implications of that evolution require most intense collaboration between design and data science. My experience so far envisioning experiences with data and algorithms shows that it is a different practice from current human-centered design. At BBVA Data & Analytics, the role of data scientists has been elevated from reactive model and A/B test developers to proactive partners who think about the implications of their work. Our singular data science teams breaks into sub-teams that partner more directly with engineers, designers, and product managers. At the moment of shaping an experience, we exploit thick data, the qualitative information that provides insights on people’s lives (see Why Big Data Needs Thick Data), big data from the aggregated behavioral data of millions of people and the small data that each individual generates. Classically, designers focus on defining the experience of the service, feature or product. They nest the concept within the larger ecosystem that relates to it. Data scientists develop the algorithms that will support that experience and measure it with A/B testing. The first few weeks in my role at BBVA Data & Analytics, I found designers and data scientists often stuck in deadlocked exchanges that typically sounded like this: The main issue was the lack of shared understanding of each other’s practice and objectives. For instance, designers transform a context into a form of experience. Data scientists transform a context with data and models into knowledge. Designers often adopt a path that adapts to a changing context and new appreciations. Data scientists employ processes similar to humber-center design but are more mechanical and less organic. They strictly follow the scientific methods with its cyclical processes of constant refinement. A properly formulated research question helps define the hypothesis and the types of models to develop in the prototyping phase. The models are the algorithms that get evaluated before they are deployed to production into what we call at BBVA Data & Analytics a “data engineâ€. Whenever the experience supported by the “data engine†does not perform as expected, the problem needs to be reformulated to continue the cyclical process of constant refinement. The scientific method is similar to any design approach that forms and makes new appreciations as new iterations are necessary. Yet, it is not an open-ended process. It has a clear start and end but no definite timeline. Data scientist Neal Lathia argues that “cross-disciplinary work is hard, until you’re speaking the same languageâ€. Additionally, I believe designers and data scientists must immerse themselves in the other’s practice to build a common rhythm. So far, I codified several important touchpoints for designers and data scientists to produce a meaningful user experience powered by algorithms. They must: This intertwined collaboration illustrates a new type of design that I am trying to articulate. In a recent article Harry West CEO at frog suggested the term ‘design of system behavior’: “Human-centered design has expanded from the design of objects (industrial design) to the design of experiences (adding interaction design, visual design, and the design of spaces) and the next step will be the design of system behavior: the design of the algorithms that determine the behavior of automated or intelligent systemsâ€â€Šâ€” Harry West So far I have argued that “living experiences†emerge at the crossroad of data science and design. An indispensable first step is for designers and data scientists is to establish a tangible vision and its outcomes (e.g. experience, solution, priorities, goals, scope and awareness of feasibility). Airbnb Director of Product Jonathan Golden calls that a vision-driven product management approach: “Your company vision is what you want the world to look like in five-plus years — outcomes are the team mandates that will help you get there.†— Jonathan Golden However, that conceptualization phase requires that visions live not just as flat perfect things for board room PowerPoint. Therefore, one of my approaches is to engage the design/science partnership to produce Design Fictions. It has similarities with Amazon’s Working Backward’ process as described by Werner Vogels: “You start with your customer and work your way backwards until you get to the minimum set of technology requirements to satisfy what you try to achieve. The goal is to drive simplicity through a continuous, explicit customer focus.â€â€Šâ€” Werner Vogels Thinking by doing with Design Fiction creates potential futures of a technology to clarify the present. Schema inspired by the Futures Cones and Matt Jones: Jumping to the End — Practical Design Fiction. Design Fiction aims at making tangible the evolution of technologies, the language used to describe them, the rituals, the magic moments, the frustrations, and why not the “offboarding experience”. It helps the different stakeholders of a project to engage with essential questions to understand what the desired experience means and why the team should build it. What are the implications of purchasing that next generation Garden Sensor? What can you do with it? What aren’t you allowed to do? What won’t you do anymore? How does a human interact with that technology the first time, and then routinely after a month, one year or more? Creative and tangible answers to these questions can come to life before a project even starts with the creation of fictional customer reviews, user manual, press release, ads. That material is a way to bring the future to present or as we say at the Near Future Laboratory: “The Design Fictions act as a totem for discussion and evaluation of changes that could bend visions of the desirable and planning of what is necessary.†At BBVA Data & Analytics, this means that I gather data scientists and designers with the objective of creating a tangible vision of their research agenda. First, we first map the ongoing lines of investigations. Then we project their evolution into 2 or 3 iterations wondering: What would the potential resulting technology look like? Where could it be used? Who would use it and for what type of experience? Each participant uses the template of a fictional ad to tell stories with practical answers to these questions. Together we group them into future concepts. We collect all the material and promote the most promising concepts. After that, we share these results internally in series of paper and video advertisements that describe the main features, attributes, characteristics of the experience from our point of view (the feasible) and the user’s point of view (the desirable). This type of fictional material allows both designers and data scientists to feel and get a practical understanding of the technology and its experience. The results help build credibility, enlist support, counter skepticism, create momentum and share a common vision. Finally, the feedback of people with different perspectives allows to anticipate opportunities and challenges. With the advance of machine learning and “artificial intelligence†(AI), it became the responsibility of both designers and data scientists to understand how to shape experiences that improve lives. Or as Greg Borenstein argues in Power to the People: How One Unknown Group of Researchers Holds the Key to Using AI to Solve Real Human Problems: “What’s needed for AI’s wide adoption is an understanding of how to build interfaces that put the power of these systems in the hands of their human users.†— Greg Borenstein That type of design of system behavior represents a future in the tight partnership between design and data science. So far in that journey of creating meaningful experiences in the machine learning era, I can articulate the following characteristics: This is an extended transcript of a talk I gave at the Design Wednesdays event at the BBVA Innovation Center in Madrid on September 21, 2016. Many thanks to the BBVA Design team for their invitation and the quality of the organization!', 'summary' => '<p>This article by author Fabien Girardin discusses the duties in his position as a designer at "B.B.V.A. Data and Analytics Company. He assists in the design of systems to provide enhanced user interactions of various software products by using comprehensive machine learning methods with teams of fellow data scientists with the company. What data scientist and designers in his company like others are doing in the new are of machine learning is upgrading their design systems into systems that uses machine learning by use of artificial intelligence, to have software in items like online shopping, banking and decision recommending predict the user's product preferences, selections, and purchase decisions based on data the machines learn from the users. In this article, Mr. Girardin’s goal is to improve on user experience with design systems his company is responsible for. He believes the new practice of providing the enhanced user satisfaction will beneficial for the customer and the company for 3 reasons. The first is that it will create new types of customer interactions. This means as new technology and software comes out, it will be the"systems designers plus data scientists" jobs to work together to create ways for the machine to learn from the user so that data can be gathered to better understand user preferences for example, movie and music genres. In other words, the more the customer uses the machine more the machine learns. The second reason is the evolution of the relationship between the user and the machine. In this article, Alexa is used as an example, and in its marketing it is used to do everything from turning on the lights to ordering pizza. It has a human and machine helper relationship and this is done by machine learning as the user continues to use the Alexa device. The third reason is in the teamwork relationship between the designers and data scientists." In the field of machine learning, "systems designers plus data scientists will be need to work together to create the system. The designers to create the user friendly interface and the data scientists enable the system to learn from the data it collects. 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Learning Era' $item_summary = 'This article by author Fabien Girardin discusses the duties in his position as a designer at "B.B.V.A. Data and Analytics Company. He assists in the design of systems to provide enhanced user interactions of various software products by using comprehensive machine learning methods with teams of fellow data scientists with the company. What data scientist and designers in his company like others are doing in the new are of machine learning is upgrading their design systems into systems that uses machine learning by use of artificial intelligence, to have software in items like online shopping, banking and decision recommending predict the user's product preferences, selections, and purchase decisions based on data the machines learn from the users. In this article, Mr. Girardin’s goal is to improve on user experience with design systems his company is responsible for. He believes the new practice of providing the enhanced user satisfaction will beneficial for the customer and the company for 3 reasons. The first is that it will create new types of customer interactions. This means as new technology and software comes out, it will be the"systems designers plus data scientists" jobs to work together to create ways for the machine to learn from the user so that data can be gathered to better understand user preferences for example, movie and music genres. In other words, the more the customer uses the machine more the machine learns. The second reason is the evolution of the relationship between the user and the machine. In this article, Alexa is used as an example, and in its marketing it is used to do everything from turning on the lights to ordering pizza. It has a human and machine helper relationship and this is done by machine learning as the user continues to use the Alexa device. The third reason is in the teamwork relationship between the designers and data scientists." In the field of machine learning, "systems designers plus data scientists will be need to work together to create the system. The designers to create the user friendly interface and the data scientists enable the system to learn from the data it collects. In conclusion, the subject of machine learning is clearly explained in this article.' $item_site_name = 'BBVA Data & Analytics' $voice_url = 'https://summarytime.com/uploads/voice_file/7190.MP3'include - APP/View/Article/landing.ctp, line 97 View::_evaluate() - CORE/Cake/View/View.php, line 948 View::_render() - CORE/Cake/View/View.php, line 910 View::render() - CORE/Cake/View/View.php, line 471 Controller::render() - CORE/Cake/Controller/Controller.php, line 954 Dispatcher::_invoke() - CORE/Cake/Routing/Dispatcher.php, line 198 Dispatcher::dispatch() - CORE/Cake/Routing/Dispatcher.php, line 165 [main] - APP/webroot/index.php, line 108
Notice (8): Undefined index: Client [APP/View/Article/landing.ctp, line 100]How can we help?Code Context<div class="popup-header"><div class="modal-title"><?php echo (($brand_details['Client']['pseudo_language_id']==2)?'איך אפשר לעזור?':"How can we help?");?><span class="assist-close"><button aria-label="Close" data-dismiss="modal" class="close close_help_desk" type="button"> <?php echo $this->Html->image('images/cls.jpg', array('alt'=>'','class'=>''));?> </button></span>$viewFile = '/home/summarytime/summurai.com/app/View/Article/landing.ctp' $dataForView = array( 'data' => array( 'MyItem' => array( 'id' => '7190', 'user_master_id' => '188', 'guid' => null, 'posted_by' => '332', 'voice_by' => '1561', 'post_market_id' => '5399', 'image_url' => 'http://www.bbvadata.com/wp-content/uploads/2016/12/discover-weekly-ml.jpg', 'title' => 'Experience Design in the Machine Learning Era', 'other_title' => '', 'description' => 'Traditionally the experience of a digital service follows pre-defined user journeys with clear states and actions. Until recently, it has been the designer’s job to create these linear workflows and transform them into understandable and unobtrusive experiences. This is the story of how that practice is about to change. Over the last 6 months, I have been working in a rather unique position at BBVA Data & Analytics, a center of excellence in financial data analysis. My job is to make the design of user experiences reach a new frontier with the emergence of machine learning techniques. My responsibility — among other things — is to bring a holistic experience design to teams of data scientists and make it an essential part of the lifecycle of algorithmic solutions (e.g. predictive models, recommender systems). In parallel, I perform creative and strategic reviews of experiences that design teams produce (e.g. online banking, online shopping, smart decision making) to steer their evolution into a future of “artificial intelligenceâ€. Practically, I boost the partnerships between teams of designers and data scientists to envision desirable and feasible experiences powered by data and algorithms. Nowadays, the design of many digital services does not only rely on data manipulation and information design but also on systems that learn from their users. If you would open the hood of these systems, you would see that behavioral data (e.g. human interactions, transactions with systems) is fed as context to algorithms that generates knowledge. An interface communicates that knowledge to enrich an experience. Ideally, that experience seeks explicit user actions or implicit sensor events to create a feedback loop that will feed the algorithm with learning material. Discovery Weekly is Spotify’s automated music recommendations “data engine†that brings two hours of custom-made music recommendations, tailored specifically to each Spotify user every Monday. The Discover Weekly’s recommender system leverages the millions playlists that Spotify users create. It gives extra weight to the company’s own experts playlists and those with more followers. The algorithm attempts to augment a person’s listening habits with those with similar tastes. It does it in three main tasks: A typical Discover Weekly playlist recommends 30 songs, a big enough set to discover music that matches with a personal taste among other false positives. That experience provokes the curation of thousands of new playlists that are fed back into the algorithm a week after to generate new recommendations. These feedback loop mechanisms typically offer ways to personalize, optimize or automate existing services. They also create opportunities to design new experiences based on recommendations, predictions or contextualization. At BBVA Data & Analytics I came up with a first non-comprehensive list: We have seen that recommender systems help discover the known unknown or even the unknown unknowns. For instance, Spotify helps discover music through a personalized experience defined on the match between an individual listening behavior and the listening behavior of hundreds of thousands of other individuals. That type of experience has at least three major design challenges. First, recommenders systems have a tendency to create a “filter bubble†that limits suggestions (e.g. products, restaurants, news items, people to connect with) to a world that is strictly linked to a profile built on past behaviors. In response, data scientists must sometimes tweak their algorithms to be less accurate and add a dose of randomness to the suggestions. Second, it is also good design practice to let an open door for users to reshape aspects of their profile that influence the discovery. I would call that feature “profile detoxâ€. Amazon for example allows users to remove items that might negatively influence the recommendations. Imagine the customers purchase gifts for others and those gifts are not necessarily material for future personalized recommendations. Finally, organizations that rely on subjective recommendation like Spotify now enlist humans to give more subjectivity and diversity to the suggested music. This approach of using humans to clean datasets or mitigate the limitations of machine learning algorithm is commonly called “Human Computation†or “Interactive Machine Learningâ€. Data and algorithms also provide means to personalize decision making. For instance at BBVA Data & Analytics we developed advanced techniques to advise BBVA customers on their finance. For example, we consider the temporal evolution of account balances to segment savings behaviors. With that technique we are able to personalize investment opportunities according to each customer’s capacity to save money. This type of algorithms that leads to decision-making needs to learn to be more precise, simply because they often rely on datasets that only give a perspective of reality. In the case of financial advisory, a customer could operate multiple accounts with other banks preventing a clear view on on saving behaviors. It proved a good design practice to let users tell implicitly or explicitly about poor information. It is the data scientist’s responsibility to express the types of feedback that enrich their models and the designer’s job to find ways to make it part of the experience. Traditionally the design of computer programs follows a binary logic with an explicit finite set of concrete and predictable states translated into a workflow. Machine learning algorithms change this with their inherent fuzzy logic. They are designed to look for patterns within a set of sample behaviors to probabilistically approximate the rules of these behaviors (see Machine Learning for Designers for a more detailed introduction to the topic). This approach comes with a certain degree imprecision and unpredictable behaviors. They often return some information on the precision of the information given. For example the booking platform Kayak predicts the evolution of prices according to the analysis of historical prices changes. Its “farecasting†algorithm is designed to return confidence on whether it is a favorable moment to purchase a ticket (see The Machine Learning Behind Farecast). A data scientist is naturally inclined to measure how accurately the algorithm predicts a value: “We predict this fare will be xâ€. That ‘prediction’ is in fact an information based on historical trends. Yet predicting is not the same as informing and a designer must consider how well such a prediction could support a user action: “Buy! this fare is likely to increaseâ€. The ‘likely’ with an overview of the price trend is an example of a “beautiful seam†in the user experience, a notion coined by Mark Weiser at the time of the Xerox Palo Alto Research Center and further developed by Chalmers and MacColl as seamful design: Seamful design is about exploiting failures and limitations to improve the experience. It is about improving the system allowing users to tell about poor recommendations. DJ Patil describes subtle techniques in Data Jujitsu. The ideal for an algorithm is to deliver high precision and recall scores. Unfortunately, precision and recall often work against each other. There is often a need to take design decisions with the trade-off between precision versus recall. For instance, in Spotify Discovery Weekly, a design decision had to be taken to define the size of playlists according to the performance of the recommender system. A large playlist highlights the confidence of Spotify to deliver a rather large inventory of 30 songs, a wide-enough set to increase the opportunities for users to stumble on perfect recommendations. Today, what we read online is based on our own behaviors and the behaviors of other users. Algorithms typically score the relevance of social and news content. The aim of these algorithms is to promote content for higher engagement or send notifications to create habits. Obviously these actions taken on our behalf are not necessarily for our own interest. In the attention economy, both designers and data scientists should learn from the anxieties, obsessions, phobias, stress and other mental burdens of the connected humans. Source: The Global Village and its Discomforts. Photo courtesy of Nicolas Nova. Arguably, we entered into the attention economy, and major online services are fighting to hook people, grap their attention for as long as possible. Their business is to keep users active as long and frequently as possible on their platforms. This leads to the development of sticky, needy experiences that often play with emotions like Fear of Missing Out (FoMO) or other obsessions to dope the user engagement. The actors of the attention economy use also techniques that promote addiction such as Variable Schedule Rewards. It is the exact same mechanisms as the ones used in slot machines. The resulting experience promotes the service’s interest (the casino) hooking people endlessly searching for the next reward. Our mobile phones have become those slot machines of notifications, alerts, messages, retweets, likes, that some of us check on an average 150 times per day if not more. Today designer can use data and algorithms to exploit cognitive vulnerabilities of people in their everyday lives. That new power raises the need for new design principles in the age of machine learning (see The ethics of good design: A principle for the connected age). There are opportunities to design a radically different experience than engagement. Indeed, an organization like a bank has the advantage of being a business that runs on data and does not need customers to spend the maximum amount of time with their services. Tristan Harris’ Time Well Spent movement is particularly inspiring in that sense. He promotes the type of experience that use data to be super-relevant or be silent. The type of technology to protect the user focus and to be respectful of people’s time. The Twitter “While you were away…†is a compelling example of that practice. Other services are good at suggesting moments to engage with them. Instead of measuring user retention, that type of experience focuses on how relevant the interactions are. Data scientist are good in detecting normal behavior and abnormal situations. At BBVA Data & Analytics we are working to promote a peace of mind to BBVA customers with mechanisms that gives a general awareness when things are fine and that trigger more detailed information on abnormal situations. More generally, we believe current generation of machine learning brings new powers to society, but also increases the responsibility of their creators. Algorithmic bias exists and may be inherent to the data sources. In consequence, there is a particular need to make algorithms more legible for people and auditable by regulators to understand their implications. Practically, this means knowledge that the an algorithm produces should safeguard the interest of their users and the results of the evaluation and the criteria used should be explained. In the previous section we have seen that the experiences powered by machine learning are not linear or based on static business and design rules. They evolves according to human behaviors with constantly updating models fed by streams of data. Each product or service becomes almost like a living, breathing thing. Or as people at Google would say: “It’s a different kind of engineeringâ€. I would argue that it is also a different kind of design. For instance, Amazon explains Echo’s braininess as a thing that “continually learns and adds more functionality over timeâ€. This description highlights the need to design the experience for systems to learn from human behavior. Consequently, beyond considering the first contact and the onboarding experience, that type of product or service requires considerations on their use after 1 hour, 1 day, 1 year, etc. If you look at the promotional video of the Edyn garden sensor you will notice the evolution of the experience from creating new habits for taking care of a garden to communicating the unknown unknowns about plants, to convey peace of mind on the key metrics, and to guarantee time well spent with some level of watering automation. That type of data product requires a responsible design that considers moments when things start to disappoint, embarrass, annoy or stop working or being useful. The design of the “offboarding experience†could become almost as important as the “onboarding experienceâ€. For instance, allegedly a third of the Fitbit users stop wearing the device within 6 months. What happens to these millions of abandoned connected objects? What happens to the data and intelligence on the individual they produced? What are the opportunities to use them in different experiences? Products characterized by an experience that evolves according to behavioral data that constantly feed algorithms (e.g. Fitbit) are living products that inevitably also have a tendency to die. Source: The Life and Death of Data Products. There are new ways to imagine the relation after a digital break-up with a product. Digital services work on an increasingly vast ecosystem of things and channels but user data have a tendency to be more centralized. Think about the notion of portable reputation that allows people to use a service based on the relation measured with another service. Looking a bit further into the near future, the recent breakthrough in Natural Language Processing, Knowledge Representation, Voice Recognition and Nature Language Production could create more subtle and stronger relations with machines. In a few iterations, Amazon Echo might start to be much more nurturing. A potential evolution that anthropologist Genevieve Bell foresees a shift from human-computer interactions to human-computer relationships in The next wave of AI is rooted in human culture and history: “So the frame there is not about recommendations, which is where much of AI is now, but is actually about nurture and care. If those become the buzzwords, then you sit in this very interesting moment of being able to pivot from talking about human-computer interactions to human-computer relationships.â€â€Šâ€” Genevieve Bell In this section we have seen that algorithms are getting closer to our everyday lives and that data provide a context for an evolving relationship. The implications of that evolution require most intense collaboration between design and data science. My experience so far envisioning experiences with data and algorithms shows that it is a different practice from current human-centered design. At BBVA Data & Analytics, the role of data scientists has been elevated from reactive model and A/B test developers to proactive partners who think about the implications of their work. Our singular data science teams breaks into sub-teams that partner more directly with engineers, designers, and product managers. At the moment of shaping an experience, we exploit thick data, the qualitative information that provides insights on people’s lives (see Why Big Data Needs Thick Data), big data from the aggregated behavioral data of millions of people and the small data that each individual generates. Classically, designers focus on defining the experience of the service, feature or product. They nest the concept within the larger ecosystem that relates to it. Data scientists develop the algorithms that will support that experience and measure it with A/B testing. The first few weeks in my role at BBVA Data & Analytics, I found designers and data scientists often stuck in deadlocked exchanges that typically sounded like this: The main issue was the lack of shared understanding of each other’s practice and objectives. For instance, designers transform a context into a form of experience. Data scientists transform a context with data and models into knowledge. Designers often adopt a path that adapts to a changing context and new appreciations. Data scientists employ processes similar to humber-center design but are more mechanical and less organic. They strictly follow the scientific methods with its cyclical processes of constant refinement. A properly formulated research question helps define the hypothesis and the types of models to develop in the prototyping phase. The models are the algorithms that get evaluated before they are deployed to production into what we call at BBVA Data & Analytics a “data engineâ€. Whenever the experience supported by the “data engine†does not perform as expected, the problem needs to be reformulated to continue the cyclical process of constant refinement. The scientific method is similar to any design approach that forms and makes new appreciations as new iterations are necessary. Yet, it is not an open-ended process. It has a clear start and end but no definite timeline. Data scientist Neal Lathia argues that “cross-disciplinary work is hard, until you’re speaking the same languageâ€. Additionally, I believe designers and data scientists must immerse themselves in the other’s practice to build a common rhythm. So far, I codified several important touchpoints for designers and data scientists to produce a meaningful user experience powered by algorithms. They must: This intertwined collaboration illustrates a new type of design that I am trying to articulate. In a recent article Harry West CEO at frog suggested the term ‘design of system behavior’: “Human-centered design has expanded from the design of objects (industrial design) to the design of experiences (adding interaction design, visual design, and the design of spaces) and the next step will be the design of system behavior: the design of the algorithms that determine the behavior of automated or intelligent systemsâ€â€Šâ€” Harry West So far I have argued that “living experiences†emerge at the crossroad of data science and design. An indispensable first step is for designers and data scientists is to establish a tangible vision and its outcomes (e.g. experience, solution, priorities, goals, scope and awareness of feasibility). Airbnb Director of Product Jonathan Golden calls that a vision-driven product management approach: “Your company vision is what you want the world to look like in five-plus years — outcomes are the team mandates that will help you get there.†— Jonathan Golden However, that conceptualization phase requires that visions live not just as flat perfect things for board room PowerPoint. Therefore, one of my approaches is to engage the design/science partnership to produce Design Fictions. It has similarities with Amazon’s Working Backward’ process as described by Werner Vogels: “You start with your customer and work your way backwards until you get to the minimum set of technology requirements to satisfy what you try to achieve. The goal is to drive simplicity through a continuous, explicit customer focus.â€â€Šâ€” Werner Vogels Thinking by doing with Design Fiction creates potential futures of a technology to clarify the present. Schema inspired by the Futures Cones and Matt Jones: Jumping to the End — Practical Design Fiction. Design Fiction aims at making tangible the evolution of technologies, the language used to describe them, the rituals, the magic moments, the frustrations, and why not the “offboarding experience”. It helps the different stakeholders of a project to engage with essential questions to understand what the desired experience means and why the team should build it. What are the implications of purchasing that next generation Garden Sensor? What can you do with it? What aren’t you allowed to do? What won’t you do anymore? How does a human interact with that technology the first time, and then routinely after a month, one year or more? Creative and tangible answers to these questions can come to life before a project even starts with the creation of fictional customer reviews, user manual, press release, ads. That material is a way to bring the future to present or as we say at the Near Future Laboratory: “The Design Fictions act as a totem for discussion and evaluation of changes that could bend visions of the desirable and planning of what is necessary.†At BBVA Data & Analytics, this means that I gather data scientists and designers with the objective of creating a tangible vision of their research agenda. First, we first map the ongoing lines of investigations. Then we project their evolution into 2 or 3 iterations wondering: What would the potential resulting technology look like? Where could it be used? Who would use it and for what type of experience? Each participant uses the template of a fictional ad to tell stories with practical answers to these questions. Together we group them into future concepts. We collect all the material and promote the most promising concepts. After that, we share these results internally in series of paper and video advertisements that describe the main features, attributes, characteristics of the experience from our point of view (the feasible) and the user’s point of view (the desirable). This type of fictional material allows both designers and data scientists to feel and get a practical understanding of the technology and its experience. The results help build credibility, enlist support, counter skepticism, create momentum and share a common vision. Finally, the feedback of people with different perspectives allows to anticipate opportunities and challenges. With the advance of machine learning and “artificial intelligence†(AI), it became the responsibility of both designers and data scientists to understand how to shape experiences that improve lives. Or as Greg Borenstein argues in Power to the People: How One Unknown Group of Researchers Holds the Key to Using AI to Solve Real Human Problems: “What’s needed for AI’s wide adoption is an understanding of how to build interfaces that put the power of these systems in the hands of their human users.†— Greg Borenstein That type of design of system behavior represents a future in the tight partnership between design and data science. So far in that journey of creating meaningful experiences in the machine learning era, I can articulate the following characteristics: This is an extended transcript of a talk I gave at the Design Wednesdays event at the BBVA Innovation Center in Madrid on September 21, 2016. Many thanks to the BBVA Design team for their invitation and the quality of the organization!', 'summary' => '<p>This article by author Fabien Girardin discusses the duties in his position as a designer at "B.B.V.A. Data and Analytics Company. He assists in the design of systems to provide enhanced user interactions of various software products by using comprehensive machine learning methods with teams of fellow data scientists with the company. What data scientist and designers in his company like others are doing in the new are of machine learning is upgrading their design systems into systems that uses machine learning by use of artificial intelligence, to have software in items like online shopping, banking and decision recommending predict the user's product preferences, selections, and purchase decisions based on data the machines learn from the users. In this article, Mr. Girardin’s goal is to improve on user experience with design systems his company is responsible for. He believes the new practice of providing the enhanced user satisfaction will beneficial for the customer and the company for 3 reasons. The first is that it will create new types of customer interactions. This means as new technology and software comes out, it will be the"systems designers plus data scientists" jobs to work together to create ways for the machine to learn from the user so that data can be gathered to better understand user preferences for example, movie and music genres. In other words, the more the customer uses the machine more the machine learns. The second reason is the evolution of the relationship between the user and the machine. In this article, Alexa is used as an example, and in its marketing it is used to do everything from turning on the lights to ordering pizza. It has a human and machine helper relationship and this is done by machine learning as the user continues to use the Alexa device. The third reason is in the teamwork relationship between the designers and data scientists." In the field of machine learning, "systems designers plus data scientists will be need to work together to create the system. The designers to create the user friendly interface and the data scientists enable the system to learn from the data it collects. 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=> '7190', 'user_master_id' => '188', 'guid' => null, 'posted_by' => '332', 'voice_by' => '1561', 'post_market_id' => '5399', 'image_url' => 'http://www.bbvadata.com/wp-content/uploads/2016/12/discover-weekly-ml.jpg', 'title' => 'Experience Design in the Machine Learning Era', 'other_title' => '', 'description' => 'Traditionally the experience of a digital service follows pre-defined user journeys with clear states and actions. Until recently, it has been the designer’s job to create these linear workflows and transform them into understandable and unobtrusive experiences. This is the story of how that practice is about to change. Over the last 6 months, I have been working in a rather unique position at BBVA Data & Analytics, a center of excellence in financial data analysis. My job is to make the design of user experiences reach a new frontier with the emergence of machine learning techniques. My responsibility — among other things — is to bring a holistic experience design to teams of data scientists and make it an essential part of the lifecycle of algorithmic solutions (e.g. predictive models, recommender systems). In parallel, I perform creative and strategic reviews of experiences that design teams produce (e.g. online banking, online shopping, smart decision making) to steer their evolution into a future of “artificial intelligenceâ€. Practically, I boost the partnerships between teams of designers and data scientists to envision desirable and feasible experiences powered by data and algorithms. Nowadays, the design of many digital services does not only rely on data manipulation and information design but also on systems that learn from their users. If you would open the hood of these systems, you would see that behavioral data (e.g. human interactions, transactions with systems) is fed as context to algorithms that generates knowledge. An interface communicates that knowledge to enrich an experience. Ideally, that experience seeks explicit user actions or implicit sensor events to create a feedback loop that will feed the algorithm with learning material. Discovery Weekly is Spotify’s automated music recommendations “data engine†that brings two hours of custom-made music recommendations, tailored specifically to each Spotify user every Monday. The Discover Weekly’s recommender system leverages the millions playlists that Spotify users create. It gives extra weight to the company’s own experts playlists and those with more followers. The algorithm attempts to augment a person’s listening habits with those with similar tastes. It does it in three main tasks: A typical Discover Weekly playlist recommends 30 songs, a big enough set to discover music that matches with a personal taste among other false positives. That experience provokes the curation of thousands of new playlists that are fed back into the algorithm a week after to generate new recommendations. These feedback loop mechanisms typically offer ways to personalize, optimize or automate existing services. They also create opportunities to design new experiences based on recommendations, predictions or contextualization. At BBVA Data & Analytics I came up with a first non-comprehensive list: We have seen that recommender systems help discover the known unknown or even the unknown unknowns. For instance, Spotify helps discover music through a personalized experience defined on the match between an individual listening behavior and the listening behavior of hundreds of thousands of other individuals. That type of experience has at least three major design challenges. First, recommenders systems have a tendency to create a “filter bubble†that limits suggestions (e.g. products, restaurants, news items, people to connect with) to a world that is strictly linked to a profile built on past behaviors. In response, data scientists must sometimes tweak their algorithms to be less accurate and add a dose of randomness to the suggestions. Second, it is also good design practice to let an open door for users to reshape aspects of their profile that influence the discovery. I would call that feature “profile detoxâ€. Amazon for example allows users to remove items that might negatively influence the recommendations. Imagine the customers purchase gifts for others and those gifts are not necessarily material for future personalized recommendations. Finally, organizations that rely on subjective recommendation like Spotify now enlist humans to give more subjectivity and diversity to the suggested music. This approach of using humans to clean datasets or mitigate the limitations of machine learning algorithm is commonly called “Human Computation†or “Interactive Machine Learningâ€. Data and algorithms also provide means to personalize decision making. For instance at BBVA Data & Analytics we developed advanced techniques to advise BBVA customers on their finance. For example, we consider the temporal evolution of account balances to segment savings behaviors. With that technique we are able to personalize investment opportunities according to each customer’s capacity to save money. This type of algorithms that leads to decision-making needs to learn to be more precise, simply because they often rely on datasets that only give a perspective of reality. In the case of financial advisory, a customer could operate multiple accounts with other banks preventing a clear view on on saving behaviors. It proved a good design practice to let users tell implicitly or explicitly about poor information. It is the data scientist’s responsibility to express the types of feedback that enrich their models and the designer’s job to find ways to make it part of the experience. Traditionally the design of computer programs follows a binary logic with an explicit finite set of concrete and predictable states translated into a workflow. Machine learning algorithms change this with their inherent fuzzy logic. They are designed to look for patterns within a set of sample behaviors to probabilistically approximate the rules of these behaviors (see Machine Learning for Designers for a more detailed introduction to the topic). This approach comes with a certain degree imprecision and unpredictable behaviors. They often return some information on the precision of the information given. For example the booking platform Kayak predicts the evolution of prices according to the analysis of historical prices changes. Its “farecasting†algorithm is designed to return confidence on whether it is a favorable moment to purchase a ticket (see The Machine Learning Behind Farecast). A data scientist is naturally inclined to measure how accurately the algorithm predicts a value: “We predict this fare will be xâ€. That ‘prediction’ is in fact an information based on historical trends. Yet predicting is not the same as informing and a designer must consider how well such a prediction could support a user action: “Buy! this fare is likely to increaseâ€. The ‘likely’ with an overview of the price trend is an example of a “beautiful seam†in the user experience, a notion coined by Mark Weiser at the time of the Xerox Palo Alto Research Center and further developed by Chalmers and MacColl as seamful design: Seamful design is about exploiting failures and limitations to improve the experience. It is about improving the system allowing users to tell about poor recommendations. DJ Patil describes subtle techniques in Data Jujitsu. The ideal for an algorithm is to deliver high precision and recall scores. Unfortunately, precision and recall often work against each other. There is often a need to take design decisions with the trade-off between precision versus recall. For instance, in Spotify Discovery Weekly, a design decision had to be taken to define the size of playlists according to the performance of the recommender system. A large playlist highlights the confidence of Spotify to deliver a rather large inventory of 30 songs, a wide-enough set to increase the opportunities for users to stumble on perfect recommendations. Today, what we read online is based on our own behaviors and the behaviors of other users. Algorithms typically score the relevance of social and news content. The aim of these algorithms is to promote content for higher engagement or send notifications to create habits. Obviously these actions taken on our behalf are not necessarily for our own interest. In the attention economy, both designers and data scientists should learn from the anxieties, obsessions, phobias, stress and other mental burdens of the connected humans. Source: The Global Village and its Discomforts. Photo courtesy of Nicolas Nova. Arguably, we entered into the attention economy, and major online services are fighting to hook people, grap their attention for as long as possible. Their business is to keep users active as long and frequently as possible on their platforms. This leads to the development of sticky, needy experiences that often play with emotions like Fear of Missing Out (FoMO) or other obsessions to dope the user engagement. The actors of the attention economy use also techniques that promote addiction such as Variable Schedule Rewards. It is the exact same mechanisms as the ones used in slot machines. The resulting experience promotes the service’s interest (the casino) hooking people endlessly searching for the next reward. Our mobile phones have become those slot machines of notifications, alerts, messages, retweets, likes, that some of us check on an average 150 times per day if not more. Today designer can use data and algorithms to exploit cognitive vulnerabilities of people in their everyday lives. That new power raises the need for new design principles in the age of machine learning (see The ethics of good design: A principle for the connected age). There are opportunities to design a radically different experience than engagement. Indeed, an organization like a bank has the advantage of being a business that runs on data and does not need customers to spend the maximum amount of time with their services. Tristan Harris’ Time Well Spent movement is particularly inspiring in that sense. He promotes the type of experience that use data to be super-relevant or be silent. The type of technology to protect the user focus and to be respectful of people’s time. The Twitter “While you were away…†is a compelling example of that practice. Other services are good at suggesting moments to engage with them. Instead of measuring user retention, that type of experience focuses on how relevant the interactions are. Data scientist are good in detecting normal behavior and abnormal situations. At BBVA Data & Analytics we are working to promote a peace of mind to BBVA customers with mechanisms that gives a general awareness when things are fine and that trigger more detailed information on abnormal situations. More generally, we believe current generation of machine learning brings new powers to society, but also increases the responsibility of their creators. Algorithmic bias exists and may be inherent to the data sources. In consequence, there is a particular need to make algorithms more legible for people and auditable by regulators to understand their implications. Practically, this means knowledge that the an algorithm produces should safeguard the interest of their users and the results of the evaluation and the criteria used should be explained. In the previous section we have seen that the experiences powered by machine learning are not linear or based on static business and design rules. They evolves according to human behaviors with constantly updating models fed by streams of data. Each product or service becomes almost like a living, breathing thing. Or as people at Google would say: “It’s a different kind of engineeringâ€. I would argue that it is also a different kind of design. For instance, Amazon explains Echo’s braininess as a thing that “continually learns and adds more functionality over timeâ€. This description highlights the need to design the experience for systems to learn from human behavior. Consequently, beyond considering the first contact and the onboarding experience, that type of product or service requires considerations on their use after 1 hour, 1 day, 1 year, etc. If you look at the promotional video of the Edyn garden sensor you will notice the evolution of the experience from creating new habits for taking care of a garden to communicating the unknown unknowns about plants, to convey peace of mind on the key metrics, and to guarantee time well spent with some level of watering automation. That type of data product requires a responsible design that considers moments when things start to disappoint, embarrass, annoy or stop working or being useful. The design of the “offboarding experience†could become almost as important as the “onboarding experienceâ€. For instance, allegedly a third of the Fitbit users stop wearing the device within 6 months. What happens to these millions of abandoned connected objects? What happens to the data and intelligence on the individual they produced? What are the opportunities to use them in different experiences? Products characterized by an experience that evolves according to behavioral data that constantly feed algorithms (e.g. Fitbit) are living products that inevitably also have a tendency to die. Source: The Life and Death of Data Products. There are new ways to imagine the relation after a digital break-up with a product. Digital services work on an increasingly vast ecosystem of things and channels but user data have a tendency to be more centralized. Think about the notion of portable reputation that allows people to use a service based on the relation measured with another service. Looking a bit further into the near future, the recent breakthrough in Natural Language Processing, Knowledge Representation, Voice Recognition and Nature Language Production could create more subtle and stronger relations with machines. In a few iterations, Amazon Echo might start to be much more nurturing. A potential evolution that anthropologist Genevieve Bell foresees a shift from human-computer interactions to human-computer relationships in The next wave of AI is rooted in human culture and history: “So the frame there is not about recommendations, which is where much of AI is now, but is actually about nurture and care. If those become the buzzwords, then you sit in this very interesting moment of being able to pivot from talking about human-computer interactions to human-computer relationships.â€â€Šâ€” Genevieve Bell In this section we have seen that algorithms are getting closer to our everyday lives and that data provide a context for an evolving relationship. The implications of that evolution require most intense collaboration between design and data science. My experience so far envisioning experiences with data and algorithms shows that it is a different practice from current human-centered design. At BBVA Data & Analytics, the role of data scientists has been elevated from reactive model and A/B test developers to proactive partners who think about the implications of their work. Our singular data science teams breaks into sub-teams that partner more directly with engineers, designers, and product managers. At the moment of shaping an experience, we exploit thick data, the qualitative information that provides insights on people’s lives (see Why Big Data Needs Thick Data), big data from the aggregated behavioral data of millions of people and the small data that each individual generates. Classically, designers focus on defining the experience of the service, feature or product. They nest the concept within the larger ecosystem that relates to it. Data scientists develop the algorithms that will support that experience and measure it with A/B testing. The first few weeks in my role at BBVA Data & Analytics, I found designers and data scientists often stuck in deadlocked exchanges that typically sounded like this: The main issue was the lack of shared understanding of each other’s practice and objectives. For instance, designers transform a context into a form of experience. Data scientists transform a context with data and models into knowledge. Designers often adopt a path that adapts to a changing context and new appreciations. Data scientists employ processes similar to humber-center design but are more mechanical and less organic. They strictly follow the scientific methods with its cyclical processes of constant refinement. A properly formulated research question helps define the hypothesis and the types of models to develop in the prototyping phase. The models are the algorithms that get evaluated before they are deployed to production into what we call at BBVA Data & Analytics a “data engineâ€. Whenever the experience supported by the “data engine†does not perform as expected, the problem needs to be reformulated to continue the cyclical process of constant refinement. The scientific method is similar to any design approach that forms and makes new appreciations as new iterations are necessary. Yet, it is not an open-ended process. It has a clear start and end but no definite timeline. Data scientist Neal Lathia argues that “cross-disciplinary work is hard, until you’re speaking the same languageâ€. Additionally, I believe designers and data scientists must immerse themselves in the other’s practice to build a common rhythm. So far, I codified several important touchpoints for designers and data scientists to produce a meaningful user experience powered by algorithms. They must: This intertwined collaboration illustrates a new type of design that I am trying to articulate. In a recent article Harry West CEO at frog suggested the term ‘design of system behavior’: “Human-centered design has expanded from the design of objects (industrial design) to the design of experiences (adding interaction design, visual design, and the design of spaces) and the next step will be the design of system behavior: the design of the algorithms that determine the behavior of automated or intelligent systemsâ€â€Šâ€” Harry West So far I have argued that “living experiences†emerge at the crossroad of data science and design. An indispensable first step is for designers and data scientists is to establish a tangible vision and its outcomes (e.g. experience, solution, priorities, goals, scope and awareness of feasibility). Airbnb Director of Product Jonathan Golden calls that a vision-driven product management approach: “Your company vision is what you want the world to look like in five-plus years — outcomes are the team mandates that will help you get there.†— Jonathan Golden However, that conceptualization phase requires that visions live not just as flat perfect things for board room PowerPoint. Therefore, one of my approaches is to engage the design/science partnership to produce Design Fictions. It has similarities with Amazon’s Working Backward’ process as described by Werner Vogels: “You start with your customer and work your way backwards until you get to the minimum set of technology requirements to satisfy what you try to achieve. The goal is to drive simplicity through a continuous, explicit customer focus.â€â€Šâ€” Werner Vogels Thinking by doing with Design Fiction creates potential futures of a technology to clarify the present. Schema inspired by the Futures Cones and Matt Jones: Jumping to the End — Practical Design Fiction. Design Fiction aims at making tangible the evolution of technologies, the language used to describe them, the rituals, the magic moments, the frustrations, and why not the “offboarding experience”. It helps the different stakeholders of a project to engage with essential questions to understand what the desired experience means and why the team should build it. What are the implications of purchasing that next generation Garden Sensor? What can you do with it? What aren’t you allowed to do? What won’t you do anymore? How does a human interact with that technology the first time, and then routinely after a month, one year or more? Creative and tangible answers to these questions can come to life before a project even starts with the creation of fictional customer reviews, user manual, press release, ads. That material is a way to bring the future to present or as we say at the Near Future Laboratory: “The Design Fictions act as a totem for discussion and evaluation of changes that could bend visions of the desirable and planning of what is necessary.†At BBVA Data & Analytics, this means that I gather data scientists and designers with the objective of creating a tangible vision of their research agenda. First, we first map the ongoing lines of investigations. Then we project their evolution into 2 or 3 iterations wondering: What would the potential resulting technology look like? Where could it be used? Who would use it and for what type of experience? Each participant uses the template of a fictional ad to tell stories with practical answers to these questions. Together we group them into future concepts. We collect all the material and promote the most promising concepts. After that, we share these results internally in series of paper and video advertisements that describe the main features, attributes, characteristics of the experience from our point of view (the feasible) and the user’s point of view (the desirable). This type of fictional material allows both designers and data scientists to feel and get a practical understanding of the technology and its experience. The results help build credibility, enlist support, counter skepticism, create momentum and share a common vision. Finally, the feedback of people with different perspectives allows to anticipate opportunities and challenges. With the advance of machine learning and “artificial intelligence†(AI), it became the responsibility of both designers and data scientists to understand how to shape experiences that improve lives. Or as Greg Borenstein argues in Power to the People: How One Unknown Group of Researchers Holds the Key to Using AI to Solve Real Human Problems: “What’s needed for AI’s wide adoption is an understanding of how to build interfaces that put the power of these systems in the hands of their human users.†— Greg Borenstein That type of design of system behavior represents a future in the tight partnership between design and data science. So far in that journey of creating meaningful experiences in the machine learning era, I can articulate the following characteristics: This is an extended transcript of a talk I gave at the Design Wednesdays event at the BBVA Innovation Center in Madrid on September 21, 2016. Many thanks to the BBVA Design team for their invitation and the quality of the organization!', 'summary' => '<p>This article by author Fabien Girardin discusses the duties in his position as a designer at "B.B.V.A. Data and Analytics Company. He assists in the design of systems to provide enhanced user interactions of various software products by using comprehensive machine learning methods with teams of fellow data scientists with the company. What data scientist and designers in his company like others are doing in the new are of machine learning is upgrading their design systems into systems that uses machine learning by use of artificial intelligence, to have software in items like online shopping, banking and decision recommending predict the user's product preferences, selections, and purchase decisions based on data the machines learn from the users. In this article, Mr. Girardin’s goal is to improve on user experience with design systems his company is responsible for. He believes the new practice of providing the enhanced user satisfaction will beneficial for the customer and the company for 3 reasons. The first is that it will create new types of customer interactions. This means as new technology and software comes out, it will be the"systems designers plus data scientists" jobs to work together to create ways for the machine to learn from the user so that data can be gathered to better understand user preferences for example, movie and music genres. In other words, the more the customer uses the machine more the machine learns. The second reason is the evolution of the relationship between the user and the machine. In this article, Alexa is used as an example, and in its marketing it is used to do everything from turning on the lights to ordering pizza. It has a human and machine helper relationship and this is done by machine learning as the user continues to use the Alexa device. The third reason is in the teamwork relationship between the designers and data scientists." In the field of machine learning, "systems designers plus data scientists will be need to work together to create the system. 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'scheduler_image' => '', 'scheduler_footer' => '', 'scheduler_footer_show' => '1', 'scheduler_reminder_sender_name' => '', 'scheduler_reminder_sender_mail' => '', 'scheduler_reminder_title' => '', 'scheduler_reminder_invite_message' => '', 'scheduler_status' => '0', 'is_coming_soon' => '0', 'is_single_summy' => '0', 'is_embed_summy' => '0', 'from_app' => '0', 'from_livedemo' => '0', 'from_podcast' => '0', 'block_editing' => '0', 'is_declined' => '0', 'is_blocked' => '0', 'is_deleted' => '0', 'created' => '2017-09-19 20:20:58', 'modified' => '2023-09-05 06:48:24' ), 'UserMaster' => array( 'password' => '*****', 'id' => '188', 'full_name' => 'Joy West', 'first_name' => '', 'last_name' => '', 'username' => '', 'email' => '[email protected]', 'gender' => '3', 'description' => '<p><span style="box-sizing: border-box; color: #494949; font-family: 'Gotham SSm', Helvetica, Arial, sans-serif; font-size: 13px; background-color: #fafafa;" data-story-id="story_5f02f4457344e4c28da759dfcbda4e23" data-timestamp="1479416503679" data-text="Michigan" data-userid="627848094442815488" data-orgid="627848094447009793">Michigan</span><img style="box-sizing: border-box; vertical-align: middle; border: 0px; color: #494949; font-family: 'Gotham SSm', Helvetica, Arial, sans-serif; font-size: 13px; background-color: #fafafa;" alt="" /><span style="background-color: #fafafa; color: #494949; font-family: 'Gotham SSm', Helvetica, Arial, sans-serif; font-size: 13px;">Michiga</span></p> <p><img style="box-sizing: border-box; vertical-align: middle; border: 0px; color: #494949; font-family: 'Gotham SSm', Helvetica, Arial, sans-serif; font-size: 13px; background-color: #fafafa;" alt="" /></p>', 'avatar_id' => '1', 'profile_picture' => '', 'profile_image' => null, 'address' => null, 'phone' => null, 'city_id' => 'Michigan', 'country_id' => 'United States', 'state' => null, 'postalcode' => null, 'latitude' => '', 'longitude' => '', 'phone_skip_count' => '0', 'isd_code' => '', 'website' => '', 'is_sms_active' => '1', 'is_email_active' => '1', 'available_credits' => '1', 'activation' => '', 'type' => '2', 'auto_approve' => '1', 'ip' => '', 'pass_code' => '1482468698585cad5ab8c57', 'company_id' => '0', 'stripe_customer_id' => '', 'is_company_user' => '0', 'change_password_count' => '0', 'is_vip' => false, 'time_zone' => 'GMT-5', 'show_on_sign_in' => '1', 'is_system_user' => '0', 'notifications_for_subcription' => '1', 'payment_platform_id' => '1', 'payment_model' => '0', 'monthly_rate' => '0', 'price_factor' => '1', 'voice_hero_price' => '0', 'monthly_free_credits' => '0', 'is_endless' => '0', 'rank_master_id' => '2', 'from_conference' => false, 'is_blogger' => false, 'sub_title' => '', 'cover_image' => '', 'curator_structure' => '', 'is_curator' => '0', 'form_curator' => false, 'curator_type' => false, 'curator_categories' => '', 'initial_cnt_followers' => '0', 'is_science' => false, 'from_client' => '0', 'is_auto_close_tab' => '1', 'property_category_id' => '0', 'blogger_id' => '0', 'from_blogger' => false, 'language_id' => '1', 'from_knowledge_pack' => '0', 'is_subscriber' => '0', 'is_archived' => '0', 'summurai_type' => '0', 'contact_url' => '', 'ad_cta' => '', 'ad_url' => '', 'ad_image' => '', 'show_in_summurai' => '1', 'is_creator' => '0', 'is_marketer' => '0', 'is_entrepreneur' => '0', 'is_aienthusiast' => '0', 'audio_purpose_onboarding' => '', 'content_type_onboarding' => '', 'audio_type_onboarding' => '', 'relevent_content_onboarding' => '', 'set_meeting_onboarding' => '', 'select_client' => '0', 'default_user' => '0', 'selected_brand_id' => '0', 'show_explainer' => '0', 'ordering' => '0', 'from_app' => '0', 'from_onboarding' => '0', 'onboarding_status' => '0', 'from_embed_playlist' => '0', 'status' => false, 'last_login' => '2018-03-13 19:27:15', 'created_by' => null, 'modified_by' => '0', 'is_blocked' => true, 'is_deleted' => false, 'created' => '2016-11-17 21:04:24', 'modified' => '2022-03-22 16:09:53' ), 'PostBy' => array( 'password' => '*****', 'id' => '332', 'full_name' => 'Shira Cinamon Lindenblat', 'first_name' => '', 'last_name' => '', 'username' => 'shiracinamon', 'email' => '[email protected]', 'gender' => '3', 'description' => '', 'avatar_id' => '16', 'profile_picture' => '', 'profile_image' => null, 'address' => null, 'phone' => '526066674', 'city_id' => null, 'country_id' => 'Israel', 'state' => null, 'postalcode' => null, 'latitude' => '', 'longitude' => '', 'phone_skip_count' => '0', 'isd_code' => '972', 'website' => '', 'is_sms_active' => '1', 'is_email_active' => '1', 'available_credits' => '22', 'activation' => '', 'type' => '1', 'auto_approve' => '0', 'ip' => '77.125.25.193', 'pass_code' => '', 'company_id' => '0', 'stripe_customer_id' => '', 'is_company_user' => '0', 'change_password_count' => '0', 'is_vip' => true, 'time_zone' => '', 'show_on_sign_in' => '1', 'is_system_user' => '0', 'notifications_for_subcription' => '1', 'payment_platform_id' => '1', 'payment_model' => '0', 'monthly_rate' => '0', 'price_factor' => '1', 'voice_hero_price' => '0', 'monthly_free_credits' => '0', 'is_endless' => '1', 'rank_master_id' => '1', 'from_conference' => false, 'is_blogger' => false, 'sub_title' => '', 'cover_image' => '', 'curator_structure' => '', 'is_curator' => '0', 'form_curator' => false, 'curator_type' => false, 'curator_categories' => '', 'initial_cnt_followers' => '0', 'is_science' => false, 'from_client' => '0', 'is_auto_close_tab' => '0', 'property_category_id' => '0', 'blogger_id' => '0', 'from_blogger' => false, 'language_id' => '1', 'from_knowledge_pack' => '0', 'is_subscriber' => '0', 'is_archived' => '0', 'summurai_type' => '0', 'contact_url' => '', 'ad_cta' => '', 'ad_url' => '', 'ad_image' => '', 'show_in_summurai' => '1', 'is_creator' => '0', 'is_marketer' => '0', 'is_entrepreneur' => '0', 'is_aienthusiast' => '0', 'audio_purpose_onboarding' => '', 'content_type_onboarding' => '', 'audio_type_onboarding' => '', 'relevent_content_onboarding' => '', 'set_meeting_onboarding' => '', 'select_client' => '0', 'default_user' => '0', 'selected_brand_id' => '0', 'show_explainer' => '0', 'ordering' => '0', 'from_app' => '0', 'from_onboarding' => '0', 'onboarding_status' => '0', 'from_embed_playlist' => '0', 'status' => false, 'last_login' => null, 'created_by' => null, 'modified_by' => '0', 'is_blocked' => false, 'is_deleted' => false, 'created' => '2017-03-08 05:41:52', 'modified' => '2022-03-22 16:09:53' ), 'VoiceBy' => array( 'password' => '*****', 'id' => '1561', 'full_name' => 'Ikwo Ibiam', 'first_name' => '', 'last_name' => '', 'username' => 'ikwo-ibiam', 'email' => '[email protected]', 'gender' => '3', 'description' => '', 'avatar_id' => '6', 'profile_picture' => '', 'profile_image' => null, 'address' => null, 'phone' => null, 'city_id' => '', 'country_id' => 'United States', 'state' => null, 'postalcode' => null, 'latitude' => '', 'longitude' => '', 'phone_skip_count' => '0', 'isd_code' => '', 'website' => '', 'is_sms_active' => '1', 'is_email_active' => 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=> '', 'image' => '1464677692_paint_palette.png', 'white_image' => '59f71af15e958_paint_palette.png', 'ordering' => '5', 'is_deleted' => '0', 'is_blocked' => '0', 'created' => '2015-11-16 13:16:06', 'modified' => '2024-01-03 22:56:04', 'created_by' => '0', 'modified_by' => '0' ), 'Client' => array( 'id' => null, 'client_secret' => null, 'parrent_id' => null, 'user_master_id' => null, 'client_name' => null, 'slug' => null, 'website' => null, 'quote' => null, 'image_url' => null, 'brand_color' => null, 'voice_file' => null, 'play_time' => null, 'direction' => null, 'client_type' => null, 'account_type' => null, 'brand_id' => null, 'image_social_url' => null, 'language_id' => null, 'brand_cat_type' => null, 'property_category_id' => null, 'secendary_color' => null, 'tag_manager' => null, 'google_pixel' => null, 'facebook_pixel' => null, 'select_client_id' => null, 'default_client_id' => null, 'curator_id' => null, 'summurai_id' => null, 'voice_hero_id' => null, 'from_summybox' => null, 'brand_type' => null, 'embed_border_color' => null, 'embed_background_color' => null, 'embed_input_color' => null, 'embed_primary_color' => null, 'embed_color_opecity' => null, 'embed_hover_color' => null, 'demo_image_name' => null, 'demo_image_url' => null, 'embed_width' => null, 'embed_height' => null, 'embed_top' => null, 'embed_left' => null, 'embed_player_title' => null, 'embed_player_title_size' => null, 'embed_mobile_link' => null, 'embed_mobile_text' => null, 'active_star' => null, 'board_sms_message' => null, 'summy_sms_message' => null, 'is_discover_content' => null, 'is_summyboards' => null, 'is_newsletter_player' => null, 'is_embedded_player' => null, 'is_full_summy_editor' => null, 'is_request_summy' => null, 'is_quick_add_summy' => null, 'is_send_to_summy_archive' => null, 'is_import_podcast' => null, 'is_playlist_report' => null, 'allow_premium_voice' => null, 'allow_export_playlist' => null, 'is_create_boards' => null, 'board_limit' => null, 'is_create_summy' => null, 'summy_limit' => null, 'brand_credit' => null, 'brand_credit_used' => null, 'default_page' => null, 'default_client_msg' => null, 'pseudo_header_color' => null, 'pseudo_main_color' => null, 'pseudo_color_opacity' => null, 'pseudo_language_id' => null, 'pseudo_feedback_show' => null, 'pseudo_brand_name_show' => null, 'pseudo_brand_link_show' => null, 'pseudo_brand_link_type' => null, 'pseudo_logo_type' => null, 'pseudo_top_logo' => null, 'pseudo_favicon' => null, 'show_pseudo_alt_footer' => null, 'pseudo_footer_color' => null, 'pseudo_footer_text_color' => null, 'pseudo_alt_footer_type' => null, 'pseudo_alt_footer_logo' => null, 'embedded_header_color' => null, 'embedded_main_color' => null, 'embedded_color_opacity' => null, 'embedded_language_id' => null, 'embedded_feedback_show' => null, 'embedded_brand_name_show' => null, 'embedded_brand_link_show' => null, 'embedded_brand_link_type' => null, 'embedded_logo_type' => null, 'embedded_top_logo' => null, 'embedded_favicon' => null, 'embed_playter_color' => null, 'embed_playter_secondary' => null, 'embed_playter_delay' => null, 'embed_playter_location' => null, 'embed_playter_allow_lead' => null, 'embed_playter_allow_sticky_bottom' => null, 'embed_playter_allow_sticky_bottom_mob' => null, 'embed_playter_hide_inline_player' => null, 'embed_playter_email_source' => null, 'embed_playter_email_name' => null, 'embed_playter_cta_text' => null, 'home_feature_section_title' => null, 'home_feature_title' => null, 'home_feature_text' => null, 'home_feature_image' => null, 'home_feature_url' => null, 'studio_promo_message' => null, 'is_set_expiration' => null, 'brand_expiration' => null, 'timezone' => null, 'from_onboarding' => null, 'from_app' => null, 'from_livedemo' => null, 'from_embed_playlist' => null, 'status' => null, 'is_blocked' => null, 'is_deleted' => null, 'created' => null, 'modified' => null ), 'Language' => array( 'id' => '1', 'title' => 'English', 'short_code' => 'en', 'direction' => '0', 'flag' => 'https://dojo.summurai.com/img/langflag/big-eng-flg.jpg', 'status' => true, 'is_blocked' => false, 'is_deleted' => false, 'created' => '2017-09-15 03:00:00', 'modified' => '2017-09-15 03:00:00' ) ) $summy_lang = array( 'Language' => array( 'id' => '1', 'title' => 'English', 'short_code' => 'en', 'direction' => '0', 'flag' => 'https://dojo.summurai.com/img/langflag/big-eng-flg.jpg', 'status' => true, 'is_blocked' => false, 'is_deleted' => false, 'created' => '2017-09-15 03:00:00', 'modified' => '2017-09-15 03:00:00' ) ) $brand_details = array() $keywords = 'data,BBVA Data,data scientists,design,experience,data scientist,good design practice,holistic experience design,data science,algorithms,Spotify Discovery Weekly,data engine,BBVA Design team,financial data analysis,machine learning,new design principles,behavioral data,data science teams,Big Data Needs,major design challenges,BBVA customers,Data scientist Neal,radically different experience,user experience,meaningful user 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Learning Era' $item_summary = 'This article by author Fabien Girardin discusses the duties in his position as a designer at "B.B.V.A. Data and Analytics Company. He assists in the design of systems to provide enhanced user interactions of various software products by using comprehensive machine learning methods with teams of fellow data scientists with the company. What data scientist and designers in his company like others are doing in the new are of machine learning is upgrading their design systems into systems that uses machine learning by use of artificial intelligence, to have software in items like online shopping, banking and decision recommending predict the user's product preferences, selections, and purchase decisions based on data the machines learn from the users. In this article, Mr. Girardin’s goal is to improve on user experience with design systems his company is responsible for. He believes the new practice of providing the enhanced user satisfaction will beneficial for the customer and the company for 3 reasons. The first is that it will create new types of customer interactions. This means as new technology and software comes out, it will be the"systems designers plus data scientists" jobs to work together to create ways for the machine to learn from the user so that data can be gathered to better understand user preferences for example, movie and music genres. In other words, the more the customer uses the machine more the machine learns. The second reason is the evolution of the relationship between the user and the machine. In this article, Alexa is used as an example, and in its marketing it is used to do everything from turning on the lights to ordering pizza. It has a human and machine helper relationship and this is done by machine learning as the user continues to use the Alexa device. The third reason is in the teamwork relationship between the designers and data scientists." In the field of machine learning, "systems designers plus data scientists will be need to work together to create the system. The designers to create the user friendly interface and the data scientists enable the system to learn from the data it collects. In conclusion, the subject of machine learning is clearly explained in this article.' $item_site_name = 'BBVA Data & Analytics' $voice_url = 'https://summarytime.com/uploads/voice_file/7190.MP3'include - APP/View/Article/landing.ctp, line 100 View::_evaluate() - CORE/Cake/View/View.php, line 948 View::_render() - CORE/Cake/View/View.php, line 910 View::render() - CORE/Cake/View/View.php, line 471 Controller::render() - CORE/Cake/Controller/Controller.php, line 954 Dispatcher::_invoke() - CORE/Cake/Routing/Dispatcher.php, line 198 Dispatcher::dispatch() - CORE/Cake/Routing/Dispatcher.php, line 165 [main] - APP/webroot/index.php, line 108
Notice (8): Undefined index: Client [APP/View/Article/landing.ctp, line 219]Code Context</div><div class="thumbs-up-part stap02 help_desk_success" style="display:none;"><div class="thumbs-icon"><?php echo $this->Html->image(($brand_details['Client']['pseudo_language_id']==2?'images/thumbs-up-icon.jpg':'images/thumbs-up-icon-eng.png'), array('alt'=>'','class'=>''));?></div>$viewFile = '/home/summarytime/summurai.com/app/View/Article/landing.ctp' $dataForView = array( 'data' => array( 'MyItem' => array( 'id' => '7190', 'user_master_id' => '188', 'guid' => null, 'posted_by' => '332', 'voice_by' => '1561', 'post_market_id' => '5399', 'image_url' => 'http://www.bbvadata.com/wp-content/uploads/2016/12/discover-weekly-ml.jpg', 'title' => 'Experience Design in the Machine Learning Era', 'other_title' => '', 'description' => 'Traditionally the experience of a digital service follows pre-defined user journeys with clear states and actions. Until recently, it has been the designer’s job to create these linear workflows and transform them into understandable and unobtrusive experiences. This is the story of how that practice is about to change. Over the last 6 months, I have been working in a rather unique position at BBVA Data & Analytics, a center of excellence in financial data analysis. My job is to make the design of user experiences reach a new frontier with the emergence of machine learning techniques. My responsibility — among other things — is to bring a holistic experience design to teams of data scientists and make it an essential part of the lifecycle of algorithmic solutions (e.g. predictive models, recommender systems). In parallel, I perform creative and strategic reviews of experiences that design teams produce (e.g. online banking, online shopping, smart decision making) to steer their evolution into a future of “artificial intelligenceâ€. Practically, I boost the partnerships between teams of designers and data scientists to envision desirable and feasible experiences powered by data and algorithms. Nowadays, the design of many digital services does not only rely on data manipulation and information design but also on systems that learn from their users. If you would open the hood of these systems, you would see that behavioral data (e.g. human interactions, transactions with systems) is fed as context to algorithms that generates knowledge. An interface communicates that knowledge to enrich an experience. Ideally, that experience seeks explicit user actions or implicit sensor events to create a feedback loop that will feed the algorithm with learning material. Discovery Weekly is Spotify’s automated music recommendations “data engine†that brings two hours of custom-made music recommendations, tailored specifically to each Spotify user every Monday. The Discover Weekly’s recommender system leverages the millions playlists that Spotify users create. It gives extra weight to the company’s own experts playlists and those with more followers. The algorithm attempts to augment a person’s listening habits with those with similar tastes. It does it in three main tasks: A typical Discover Weekly playlist recommends 30 songs, a big enough set to discover music that matches with a personal taste among other false positives. That experience provokes the curation of thousands of new playlists that are fed back into the algorithm a week after to generate new recommendations. These feedback loop mechanisms typically offer ways to personalize, optimize or automate existing services. They also create opportunities to design new experiences based on recommendations, predictions or contextualization. At BBVA Data & Analytics I came up with a first non-comprehensive list: We have seen that recommender systems help discover the known unknown or even the unknown unknowns. For instance, Spotify helps discover music through a personalized experience defined on the match between an individual listening behavior and the listening behavior of hundreds of thousands of other individuals. That type of experience has at least three major design challenges. First, recommenders systems have a tendency to create a “filter bubble†that limits suggestions (e.g. products, restaurants, news items, people to connect with) to a world that is strictly linked to a profile built on past behaviors. In response, data scientists must sometimes tweak their algorithms to be less accurate and add a dose of randomness to the suggestions. Second, it is also good design practice to let an open door for users to reshape aspects of their profile that influence the discovery. I would call that feature “profile detoxâ€. Amazon for example allows users to remove items that might negatively influence the recommendations. Imagine the customers purchase gifts for others and those gifts are not necessarily material for future personalized recommendations. Finally, organizations that rely on subjective recommendation like Spotify now enlist humans to give more subjectivity and diversity to the suggested music. This approach of using humans to clean datasets or mitigate the limitations of machine learning algorithm is commonly called “Human Computation†or “Interactive Machine Learningâ€. Data and algorithms also provide means to personalize decision making. For instance at BBVA Data & Analytics we developed advanced techniques to advise BBVA customers on their finance. For example, we consider the temporal evolution of account balances to segment savings behaviors. With that technique we are able to personalize investment opportunities according to each customer’s capacity to save money. This type of algorithms that leads to decision-making needs to learn to be more precise, simply because they often rely on datasets that only give a perspective of reality. In the case of financial advisory, a customer could operate multiple accounts with other banks preventing a clear view on on saving behaviors. It proved a good design practice to let users tell implicitly or explicitly about poor information. It is the data scientist’s responsibility to express the types of feedback that enrich their models and the designer’s job to find ways to make it part of the experience. Traditionally the design of computer programs follows a binary logic with an explicit finite set of concrete and predictable states translated into a workflow. Machine learning algorithms change this with their inherent fuzzy logic. They are designed to look for patterns within a set of sample behaviors to probabilistically approximate the rules of these behaviors (see Machine Learning for Designers for a more detailed introduction to the topic). This approach comes with a certain degree imprecision and unpredictable behaviors. They often return some information on the precision of the information given. For example the booking platform Kayak predicts the evolution of prices according to the analysis of historical prices changes. Its “farecasting†algorithm is designed to return confidence on whether it is a favorable moment to purchase a ticket (see The Machine Learning Behind Farecast). A data scientist is naturally inclined to measure how accurately the algorithm predicts a value: “We predict this fare will be xâ€. That ‘prediction’ is in fact an information based on historical trends. Yet predicting is not the same as informing and a designer must consider how well such a prediction could support a user action: “Buy! this fare is likely to increaseâ€. The ‘likely’ with an overview of the price trend is an example of a “beautiful seam†in the user experience, a notion coined by Mark Weiser at the time of the Xerox Palo Alto Research Center and further developed by Chalmers and MacColl as seamful design: Seamful design is about exploiting failures and limitations to improve the experience. It is about improving the system allowing users to tell about poor recommendations. DJ Patil describes subtle techniques in Data Jujitsu. The ideal for an algorithm is to deliver high precision and recall scores. Unfortunately, precision and recall often work against each other. There is often a need to take design decisions with the trade-off between precision versus recall. For instance, in Spotify Discovery Weekly, a design decision had to be taken to define the size of playlists according to the performance of the recommender system. A large playlist highlights the confidence of Spotify to deliver a rather large inventory of 30 songs, a wide-enough set to increase the opportunities for users to stumble on perfect recommendations. Today, what we read online is based on our own behaviors and the behaviors of other users. Algorithms typically score the relevance of social and news content. The aim of these algorithms is to promote content for higher engagement or send notifications to create habits. Obviously these actions taken on our behalf are not necessarily for our own interest. In the attention economy, both designers and data scientists should learn from the anxieties, obsessions, phobias, stress and other mental burdens of the connected humans. Source: The Global Village and its Discomforts. Photo courtesy of Nicolas Nova. Arguably, we entered into the attention economy, and major online services are fighting to hook people, grap their attention for as long as possible. Their business is to keep users active as long and frequently as possible on their platforms. This leads to the development of sticky, needy experiences that often play with emotions like Fear of Missing Out (FoMO) or other obsessions to dope the user engagement. The actors of the attention economy use also techniques that promote addiction such as Variable Schedule Rewards. It is the exact same mechanisms as the ones used in slot machines. The resulting experience promotes the service’s interest (the casino) hooking people endlessly searching for the next reward. Our mobile phones have become those slot machines of notifications, alerts, messages, retweets, likes, that some of us check on an average 150 times per day if not more. Today designer can use data and algorithms to exploit cognitive vulnerabilities of people in their everyday lives. That new power raises the need for new design principles in the age of machine learning (see The ethics of good design: A principle for the connected age). There are opportunities to design a radically different experience than engagement. Indeed, an organization like a bank has the advantage of being a business that runs on data and does not need customers to spend the maximum amount of time with their services. Tristan Harris’ Time Well Spent movement is particularly inspiring in that sense. He promotes the type of experience that use data to be super-relevant or be silent. The type of technology to protect the user focus and to be respectful of people’s time. The Twitter “While you were away…†is a compelling example of that practice. Other services are good at suggesting moments to engage with them. Instead of measuring user retention, that type of experience focuses on how relevant the interactions are. Data scientist are good in detecting normal behavior and abnormal situations. At BBVA Data & Analytics we are working to promote a peace of mind to BBVA customers with mechanisms that gives a general awareness when things are fine and that trigger more detailed information on abnormal situations. More generally, we believe current generation of machine learning brings new powers to society, but also increases the responsibility of their creators. Algorithmic bias exists and may be inherent to the data sources. In consequence, there is a particular need to make algorithms more legible for people and auditable by regulators to understand their implications. Practically, this means knowledge that the an algorithm produces should safeguard the interest of their users and the results of the evaluation and the criteria used should be explained. In the previous section we have seen that the experiences powered by machine learning are not linear or based on static business and design rules. They evolves according to human behaviors with constantly updating models fed by streams of data. Each product or service becomes almost like a living, breathing thing. Or as people at Google would say: “It’s a different kind of engineeringâ€. I would argue that it is also a different kind of design. For instance, Amazon explains Echo’s braininess as a thing that “continually learns and adds more functionality over timeâ€. This description highlights the need to design the experience for systems to learn from human behavior. Consequently, beyond considering the first contact and the onboarding experience, that type of product or service requires considerations on their use after 1 hour, 1 day, 1 year, etc. If you look at the promotional video of the Edyn garden sensor you will notice the evolution of the experience from creating new habits for taking care of a garden to communicating the unknown unknowns about plants, to convey peace of mind on the key metrics, and to guarantee time well spent with some level of watering automation. That type of data product requires a responsible design that considers moments when things start to disappoint, embarrass, annoy or stop working or being useful. The design of the “offboarding experience†could become almost as important as the “onboarding experienceâ€. For instance, allegedly a third of the Fitbit users stop wearing the device within 6 months. What happens to these millions of abandoned connected objects? What happens to the data and intelligence on the individual they produced? What are the opportunities to use them in different experiences? Products characterized by an experience that evolves according to behavioral data that constantly feed algorithms (e.g. Fitbit) are living products that inevitably also have a tendency to die. Source: The Life and Death of Data Products. There are new ways to imagine the relation after a digital break-up with a product. Digital services work on an increasingly vast ecosystem of things and channels but user data have a tendency to be more centralized. Think about the notion of portable reputation that allows people to use a service based on the relation measured with another service. Looking a bit further into the near future, the recent breakthrough in Natural Language Processing, Knowledge Representation, Voice Recognition and Nature Language Production could create more subtle and stronger relations with machines. In a few iterations, Amazon Echo might start to be much more nurturing. A potential evolution that anthropologist Genevieve Bell foresees a shift from human-computer interactions to human-computer relationships in The next wave of AI is rooted in human culture and history: “So the frame there is not about recommendations, which is where much of AI is now, but is actually about nurture and care. If those become the buzzwords, then you sit in this very interesting moment of being able to pivot from talking about human-computer interactions to human-computer relationships.â€â€Šâ€” Genevieve Bell In this section we have seen that algorithms are getting closer to our everyday lives and that data provide a context for an evolving relationship. The implications of that evolution require most intense collaboration between design and data science. My experience so far envisioning experiences with data and algorithms shows that it is a different practice from current human-centered design. At BBVA Data & Analytics, the role of data scientists has been elevated from reactive model and A/B test developers to proactive partners who think about the implications of their work. Our singular data science teams breaks into sub-teams that partner more directly with engineers, designers, and product managers. At the moment of shaping an experience, we exploit thick data, the qualitative information that provides insights on people’s lives (see Why Big Data Needs Thick Data), big data from the aggregated behavioral data of millions of people and the small data that each individual generates. Classically, designers focus on defining the experience of the service, feature or product. They nest the concept within the larger ecosystem that relates to it. Data scientists develop the algorithms that will support that experience and measure it with A/B testing. The first few weeks in my role at BBVA Data & Analytics, I found designers and data scientists often stuck in deadlocked exchanges that typically sounded like this: The main issue was the lack of shared understanding of each other’s practice and objectives. For instance, designers transform a context into a form of experience. Data scientists transform a context with data and models into knowledge. Designers often adopt a path that adapts to a changing context and new appreciations. Data scientists employ processes similar to humber-center design but are more mechanical and less organic. They strictly follow the scientific methods with its cyclical processes of constant refinement. A properly formulated research question helps define the hypothesis and the types of models to develop in the prototyping phase. The models are the algorithms that get evaluated before they are deployed to production into what we call at BBVA Data & Analytics a “data engineâ€. Whenever the experience supported by the “data engine†does not perform as expected, the problem needs to be reformulated to continue the cyclical process of constant refinement. The scientific method is similar to any design approach that forms and makes new appreciations as new iterations are necessary. Yet, it is not an open-ended process. It has a clear start and end but no definite timeline. Data scientist Neal Lathia argues that “cross-disciplinary work is hard, until you’re speaking the same languageâ€. Additionally, I believe designers and data scientists must immerse themselves in the other’s practice to build a common rhythm. So far, I codified several important touchpoints for designers and data scientists to produce a meaningful user experience powered by algorithms. They must: This intertwined collaboration illustrates a new type of design that I am trying to articulate. In a recent article Harry West CEO at frog suggested the term ‘design of system behavior’: “Human-centered design has expanded from the design of objects (industrial design) to the design of experiences (adding interaction design, visual design, and the design of spaces) and the next step will be the design of system behavior: the design of the algorithms that determine the behavior of automated or intelligent systemsâ€â€Šâ€” Harry West So far I have argued that “living experiences†emerge at the crossroad of data science and design. An indispensable first step is for designers and data scientists is to establish a tangible vision and its outcomes (e.g. experience, solution, priorities, goals, scope and awareness of feasibility). Airbnb Director of Product Jonathan Golden calls that a vision-driven product management approach: “Your company vision is what you want the world to look like in five-plus years — outcomes are the team mandates that will help you get there.†— Jonathan Golden However, that conceptualization phase requires that visions live not just as flat perfect things for board room PowerPoint. Therefore, one of my approaches is to engage the design/science partnership to produce Design Fictions. It has similarities with Amazon’s Working Backward’ process as described by Werner Vogels: “You start with your customer and work your way backwards until you get to the minimum set of technology requirements to satisfy what you try to achieve. The goal is to drive simplicity through a continuous, explicit customer focus.â€â€Šâ€” Werner Vogels Thinking by doing with Design Fiction creates potential futures of a technology to clarify the present. Schema inspired by the Futures Cones and Matt Jones: Jumping to the End — Practical Design Fiction. Design Fiction aims at making tangible the evolution of technologies, the language used to describe them, the rituals, the magic moments, the frustrations, and why not the “offboarding experience”. It helps the different stakeholders of a project to engage with essential questions to understand what the desired experience means and why the team should build it. What are the implications of purchasing that next generation Garden Sensor? What can you do with it? What aren’t you allowed to do? What won’t you do anymore? How does a human interact with that technology the first time, and then routinely after a month, one year or more? Creative and tangible answers to these questions can come to life before a project even starts with the creation of fictional customer reviews, user manual, press release, ads. That material is a way to bring the future to present or as we say at the Near Future Laboratory: “The Design Fictions act as a totem for discussion and evaluation of changes that could bend visions of the desirable and planning of what is necessary.†At BBVA Data & Analytics, this means that I gather data scientists and designers with the objective of creating a tangible vision of their research agenda. First, we first map the ongoing lines of investigations. Then we project their evolution into 2 or 3 iterations wondering: What would the potential resulting technology look like? Where could it be used? Who would use it and for what type of experience? Each participant uses the template of a fictional ad to tell stories with practical answers to these questions. Together we group them into future concepts. We collect all the material and promote the most promising concepts. After that, we share these results internally in series of paper and video advertisements that describe the main features, attributes, characteristics of the experience from our point of view (the feasible) and the user’s point of view (the desirable). This type of fictional material allows both designers and data scientists to feel and get a practical understanding of the technology and its experience. The results help build credibility, enlist support, counter skepticism, create momentum and share a common vision. Finally, the feedback of people with different perspectives allows to anticipate opportunities and challenges. With the advance of machine learning and “artificial intelligence†(AI), it became the responsibility of both designers and data scientists to understand how to shape experiences that improve lives. Or as Greg Borenstein argues in Power to the People: How One Unknown Group of Researchers Holds the Key to Using AI to Solve Real Human Problems: “What’s needed for AI’s wide adoption is an understanding of how to build interfaces that put the power of these systems in the hands of their human users.†— Greg Borenstein That type of design of system behavior represents a future in the tight partnership between design and data science. So far in that journey of creating meaningful experiences in the machine learning era, I can articulate the following characteristics: This is an extended transcript of a talk I gave at the Design Wednesdays event at the BBVA Innovation Center in Madrid on September 21, 2016. Many thanks to the BBVA Design team for their invitation and the quality of the organization!', 'summary' => '<p>This article by author Fabien Girardin discusses the duties in his position as a designer at "B.B.V.A. Data and Analytics Company. He assists in the design of systems to provide enhanced user interactions of various software products by using comprehensive machine learning methods with teams of fellow data scientists with the company. What data scientist and designers in his company like others are doing in the new are of machine learning is upgrading their design systems into systems that uses machine learning by use of artificial intelligence, to have software in items like online shopping, banking and decision recommending predict the user's product preferences, selections, and purchase decisions based on data the machines learn from the users. In this article, Mr. Girardin’s goal is to improve on user experience with design systems his company is responsible for. He believes the new practice of providing the enhanced user satisfaction will beneficial for the customer and the company for 3 reasons. The first is that it will create new types of customer interactions. This means as new technology and software comes out, it will be the"systems designers plus data scientists" jobs to work together to create ways for the machine to learn from the user so that data can be gathered to better understand user preferences for example, movie and music genres. In other words, the more the customer uses the machine more the machine learns. The second reason is the evolution of the relationship between the user and the machine. In this article, Alexa is used as an example, and in its marketing it is used to do everything from turning on the lights to ordering pizza. It has a human and machine helper relationship and this is done by machine learning as the user continues to use the Alexa device. The third reason is in the teamwork relationship between the designers and data scientists." In the field of machine learning, "systems designers plus data scientists will be need to work together to create the system. The designers to create the user friendly interface and the data scientists enable the system to learn from the data it collects. 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Until recently, it has been the designer’s job to create these linear workflows and transform them into understandable and unobtrusive experiences. This is the story of how that practice is about to change. Over the last 6 months, I have been working in a rather unique position at BBVA Data & Analytics, a center of excellence in financial data analysis. My job is to make the design of user experiences reach a new frontier with the emergence of machine learning techniques. My responsibility — among other things — is to bring a holistic experience design to teams of data scientists and make it an essential part of the lifecycle of algorithmic solutions (e.g. predictive models, recommender systems). In parallel, I perform creative and strategic reviews of experiences that design teams produce (e.g. online banking, online shopping, smart decision making) to steer their evolution into a future of “artificial intelligenceâ€. Practically, I boost the partnerships between teams of designers and data scientists to envision desirable and feasible experiences powered by data and algorithms. Nowadays, the design of many digital services does not only rely on data manipulation and information design but also on systems that learn from their users. If you would open the hood of these systems, you would see that behavioral data (e.g. human interactions, transactions with systems) is fed as context to algorithms that generates knowledge. An interface communicates that knowledge to enrich an experience. Ideally, that experience seeks explicit user actions or implicit sensor events to create a feedback loop that will feed the algorithm with learning material. Discovery Weekly is Spotify’s automated music recommendations “data engine†that brings two hours of custom-made music recommendations, tailored specifically to each Spotify user every Monday. The Discover Weekly’s recommender system leverages the millions playlists that Spotify users create. It gives extra weight to the company’s own experts playlists and those with more followers. The algorithm attempts to augment a person’s listening habits with those with similar tastes. It does it in three main tasks: A typical Discover Weekly playlist recommends 30 songs, a big enough set to discover music that matches with a personal taste among other false positives. That experience provokes the curation of thousands of new playlists that are fed back into the algorithm a week after to generate new recommendations. These feedback loop mechanisms typically offer ways to personalize, optimize or automate existing services. They also create opportunities to design new experiences based on recommendations, predictions or contextualization. At BBVA Data & Analytics I came up with a first non-comprehensive list: We have seen that recommender systems help discover the known unknown or even the unknown unknowns. For instance, Spotify helps discover music through a personalized experience defined on the match between an individual listening behavior and the listening behavior of hundreds of thousands of other individuals. That type of experience has at least three major design challenges. First, recommenders systems have a tendency to create a “filter bubble†that limits suggestions (e.g. products, restaurants, news items, people to connect with) to a world that is strictly linked to a profile built on past behaviors. In response, data scientists must sometimes tweak their algorithms to be less accurate and add a dose of randomness to the suggestions. Second, it is also good design practice to let an open door for users to reshape aspects of their profile that influence the discovery. I would call that feature “profile detoxâ€. Amazon for example allows users to remove items that might negatively influence the recommendations. Imagine the customers purchase gifts for others and those gifts are not necessarily material for future personalized recommendations. Finally, organizations that rely on subjective recommendation like Spotify now enlist humans to give more subjectivity and diversity to the suggested music. This approach of using humans to clean datasets or mitigate the limitations of machine learning algorithm is commonly called “Human Computation†or “Interactive Machine Learningâ€. Data and algorithms also provide means to personalize decision making. For instance at BBVA Data & Analytics we developed advanced techniques to advise BBVA customers on their finance. For example, we consider the temporal evolution of account balances to segment savings behaviors. With that technique we are able to personalize investment opportunities according to each customer’s capacity to save money. This type of algorithms that leads to decision-making needs to learn to be more precise, simply because they often rely on datasets that only give a perspective of reality. In the case of financial advisory, a customer could operate multiple accounts with other banks preventing a clear view on on saving behaviors. It proved a good design practice to let users tell implicitly or explicitly about poor information. It is the data scientist’s responsibility to express the types of feedback that enrich their models and the designer’s job to find ways to make it part of the experience. Traditionally the design of computer programs follows a binary logic with an explicit finite set of concrete and predictable states translated into a workflow. Machine learning algorithms change this with their inherent fuzzy logic. They are designed to look for patterns within a set of sample behaviors to probabilistically approximate the rules of these behaviors (see Machine Learning for Designers for a more detailed introduction to the topic). This approach comes with a certain degree imprecision and unpredictable behaviors. They often return some information on the precision of the information given. For example the booking platform Kayak predicts the evolution of prices according to the analysis of historical prices changes. Its “farecasting†algorithm is designed to return confidence on whether it is a favorable moment to purchase a ticket (see The Machine Learning Behind Farecast). A data scientist is naturally inclined to measure how accurately the algorithm predicts a value: “We predict this fare will be xâ€. That ‘prediction’ is in fact an information based on historical trends. Yet predicting is not the same as informing and a designer must consider how well such a prediction could support a user action: “Buy! this fare is likely to increaseâ€. The ‘likely’ with an overview of the price trend is an example of a “beautiful seam†in the user experience, a notion coined by Mark Weiser at the time of the Xerox Palo Alto Research Center and further developed by Chalmers and MacColl as seamful design: Seamful design is about exploiting failures and limitations to improve the experience. It is about improving the system allowing users to tell about poor recommendations. DJ Patil describes subtle techniques in Data Jujitsu. The ideal for an algorithm is to deliver high precision and recall scores. Unfortunately, precision and recall often work against each other. There is often a need to take design decisions with the trade-off between precision versus recall. For instance, in Spotify Discovery Weekly, a design decision had to be taken to define the size of playlists according to the performance of the recommender system. A large playlist highlights the confidence of Spotify to deliver a rather large inventory of 30 songs, a wide-enough set to increase the opportunities for users to stumble on perfect recommendations. Today, what we read online is based on our own behaviors and the behaviors of other users. Algorithms typically score the relevance of social and news content. The aim of these algorithms is to promote content for higher engagement or send notifications to create habits. Obviously these actions taken on our behalf are not necessarily for our own interest. In the attention economy, both designers and data scientists should learn from the anxieties, obsessions, phobias, stress and other mental burdens of the connected humans. Source: The Global Village and its Discomforts. Photo courtesy of Nicolas Nova. Arguably, we entered into the attention economy, and major online services are fighting to hook people, grap their attention for as long as possible. Their business is to keep users active as long and frequently as possible on their platforms. This leads to the development of sticky, needy experiences that often play with emotions like Fear of Missing Out (FoMO) or other obsessions to dope the user engagement. The actors of the attention economy use also techniques that promote addiction such as Variable Schedule Rewards. It is the exact same mechanisms as the ones used in slot machines. The resulting experience promotes the service’s interest (the casino) hooking people endlessly searching for the next reward. Our mobile phones have become those slot machines of notifications, alerts, messages, retweets, likes, that some of us check on an average 150 times per day if not more. Today designer can use data and algorithms to exploit cognitive vulnerabilities of people in their everyday lives. That new power raises the need for new design principles in the age of machine learning (see The ethics of good design: A principle for the connected age). There are opportunities to design a radically different experience than engagement. Indeed, an organization like a bank has the advantage of being a business that runs on data and does not need customers to spend the maximum amount of time with their services. Tristan Harris’ Time Well Spent movement is particularly inspiring in that sense. He promotes the type of experience that use data to be super-relevant or be silent. The type of technology to protect the user focus and to be respectful of people’s time. The Twitter “While you were away…†is a compelling example of that practice. Other services are good at suggesting moments to engage with them. Instead of measuring user retention, that type of experience focuses on how relevant the interactions are. Data scientist are good in detecting normal behavior and abnormal situations. At BBVA Data & Analytics we are working to promote a peace of mind to BBVA customers with mechanisms that gives a general awareness when things are fine and that trigger more detailed information on abnormal situations. More generally, we believe current generation of machine learning brings new powers to society, but also increases the responsibility of their creators. Algorithmic bias exists and may be inherent to the data sources. In consequence, there is a particular need to make algorithms more legible for people and auditable by regulators to understand their implications. Practically, this means knowledge that the an algorithm produces should safeguard the interest of their users and the results of the evaluation and the criteria used should be explained. In the previous section we have seen that the experiences powered by machine learning are not linear or based on static business and design rules. They evolves according to human behaviors with constantly updating models fed by streams of data. Each product or service becomes almost like a living, breathing thing. Or as people at Google would say: “It’s a different kind of engineeringâ€. I would argue that it is also a different kind of design. For instance, Amazon explains Echo’s braininess as a thing that “continually learns and adds more functionality over timeâ€. This description highlights the need to design the experience for systems to learn from human behavior. Consequently, beyond considering the first contact and the onboarding experience, that type of product or service requires considerations on their use after 1 hour, 1 day, 1 year, etc. If you look at the promotional video of the Edyn garden sensor you will notice the evolution of the experience from creating new habits for taking care of a garden to communicating the unknown unknowns about plants, to convey peace of mind on the key metrics, and to guarantee time well spent with some level of watering automation. That type of data product requires a responsible design that considers moments when things start to disappoint, embarrass, annoy or stop working or being useful. The design of the “offboarding experience†could become almost as important as the “onboarding experienceâ€. For instance, allegedly a third of the Fitbit users stop wearing the device within 6 months. What happens to these millions of abandoned connected objects? What happens to the data and intelligence on the individual they produced? What are the opportunities to use them in different experiences? Products characterized by an experience that evolves according to behavioral data that constantly feed algorithms (e.g. Fitbit) are living products that inevitably also have a tendency to die. Source: The Life and Death of Data Products. There are new ways to imagine the relation after a digital break-up with a product. Digital services work on an increasingly vast ecosystem of things and channels but user data have a tendency to be more centralized. Think about the notion of portable reputation that allows people to use a service based on the relation measured with another service. Looking a bit further into the near future, the recent breakthrough in Natural Language Processing, Knowledge Representation, Voice Recognition and Nature Language Production could create more subtle and stronger relations with machines. In a few iterations, Amazon Echo might start to be much more nurturing. A potential evolution that anthropologist Genevieve Bell foresees a shift from human-computer interactions to human-computer relationships in The next wave of AI is rooted in human culture and history: “So the frame there is not about recommendations, which is where much of AI is now, but is actually about nurture and care. If those become the buzzwords, then you sit in this very interesting moment of being able to pivot from talking about human-computer interactions to human-computer relationships.â€â€Šâ€” Genevieve Bell In this section we have seen that algorithms are getting closer to our everyday lives and that data provide a context for an evolving relationship. The implications of that evolution require most intense collaboration between design and data science. My experience so far envisioning experiences with data and algorithms shows that it is a different practice from current human-centered design. At BBVA Data & Analytics, the role of data scientists has been elevated from reactive model and A/B test developers to proactive partners who think about the implications of their work. Our singular data science teams breaks into sub-teams that partner more directly with engineers, designers, and product managers. At the moment of shaping an experience, we exploit thick data, the qualitative information that provides insights on people’s lives (see Why Big Data Needs Thick Data), big data from the aggregated behavioral data of millions of people and the small data that each individual generates. Classically, designers focus on defining the experience of the service, feature or product. They nest the concept within the larger ecosystem that relates to it. Data scientists develop the algorithms that will support that experience and measure it with A/B testing. The first few weeks in my role at BBVA Data & Analytics, I found designers and data scientists often stuck in deadlocked exchanges that typically sounded like this: The main issue was the lack of shared understanding of each other’s practice and objectives. For instance, designers transform a context into a form of experience. Data scientists transform a context with data and models into knowledge. Designers often adopt a path that adapts to a changing context and new appreciations. Data scientists employ processes similar to humber-center design but are more mechanical and less organic. They strictly follow the scientific methods with its cyclical processes of constant refinement. A properly formulated research question helps define the hypothesis and the types of models to develop in the prototyping phase. The models are the algorithms that get evaluated before they are deployed to production into what we call at BBVA Data & Analytics a “data engineâ€. Whenever the experience supported by the “data engine†does not perform as expected, the problem needs to be reformulated to continue the cyclical process of constant refinement. The scientific method is similar to any design approach that forms and makes new appreciations as new iterations are necessary. Yet, it is not an open-ended process. It has a clear start and end but no definite timeline. Data scientist Neal Lathia argues that “cross-disciplinary work is hard, until you’re speaking the same languageâ€. Additionally, I believe designers and data scientists must immerse themselves in the other’s practice to build a common rhythm. So far, I codified several important touchpoints for designers and data scientists to produce a meaningful user experience powered by algorithms. They must: This intertwined collaboration illustrates a new type of design that I am trying to articulate. In a recent article Harry West CEO at frog suggested the term ‘design of system behavior’: “Human-centered design has expanded from the design of objects (industrial design) to the design of experiences (adding interaction design, visual design, and the design of spaces) and the next step will be the design of system behavior: the design of the algorithms that determine the behavior of automated or intelligent systemsâ€â€Šâ€” Harry West So far I have argued that “living experiences†emerge at the crossroad of data science and design. An indispensable first step is for designers and data scientists is to establish a tangible vision and its outcomes (e.g. experience, solution, priorities, goals, scope and awareness of feasibility). Airbnb Director of Product Jonathan Golden calls that a vision-driven product management approach: “Your company vision is what you want the world to look like in five-plus years — outcomes are the team mandates that will help you get there.†— Jonathan Golden However, that conceptualization phase requires that visions live not just as flat perfect things for board room PowerPoint. Therefore, one of my approaches is to engage the design/science partnership to produce Design Fictions. It has similarities with Amazon’s Working Backward’ process as described by Werner Vogels: “You start with your customer and work your way backwards until you get to the minimum set of technology requirements to satisfy what you try to achieve. The goal is to drive simplicity through a continuous, explicit customer focus.â€â€Šâ€” Werner Vogels Thinking by doing with Design Fiction creates potential futures of a technology to clarify the present. Schema inspired by the Futures Cones and Matt Jones: Jumping to the End — Practical Design Fiction. Design Fiction aims at making tangible the evolution of technologies, the language used to describe them, the rituals, the magic moments, the frustrations, and why not the “offboarding experience”. It helps the different stakeholders of a project to engage with essential questions to understand what the desired experience means and why the team should build it. What are the implications of purchasing that next generation Garden Sensor? What can you do with it? What aren’t you allowed to do? What won’t you do anymore? How does a human interact with that technology the first time, and then routinely after a month, one year or more? Creative and tangible answers to these questions can come to life before a project even starts with the creation of fictional customer reviews, user manual, press release, ads. That material is a way to bring the future to present or as we say at the Near Future Laboratory: “The Design Fictions act as a totem for discussion and evaluation of changes that could bend visions of the desirable and planning of what is necessary.†At BBVA Data & Analytics, this means that I gather data scientists and designers with the objective of creating a tangible vision of their research agenda. First, we first map the ongoing lines of investigations. Then we project their evolution into 2 or 3 iterations wondering: What would the potential resulting technology look like? Where could it be used? Who would use it and for what type of experience? Each participant uses the template of a fictional ad to tell stories with practical answers to these questions. Together we group them into future concepts. We collect all the material and promote the most promising concepts. After that, we share these results internally in series of paper and video advertisements that describe the main features, attributes, characteristics of the experience from our point of view (the feasible) and the user’s point of view (the desirable). This type of fictional material allows both designers and data scientists to feel and get a practical understanding of the technology and its experience. The results help build credibility, enlist support, counter skepticism, create momentum and share a common vision. Finally, the feedback of people with different perspectives allows to anticipate opportunities and challenges. With the advance of machine learning and “artificial intelligence†(AI), it became the responsibility of both designers and data scientists to understand how to shape experiences that improve lives. Or as Greg Borenstein argues in Power to the People: How One Unknown Group of Researchers Holds the Key to Using AI to Solve Real Human Problems: “What’s needed for AI’s wide adoption is an understanding of how to build interfaces that put the power of these systems in the hands of their human users.†— Greg Borenstein That type of design of system behavior represents a future in the tight partnership between design and data science. So far in that journey of creating meaningful experiences in the machine learning era, I can articulate the following characteristics: This is an extended transcript of a talk I gave at the Design Wednesdays event at the BBVA Innovation Center in Madrid on September 21, 2016. Many thanks to the BBVA Design team for their invitation and the quality of the organization!', 'summary' => '<p>This article by author Fabien Girardin discusses the duties in his position as a designer at "B.B.V.A. Data and Analytics Company. He assists in the design of systems to provide enhanced user interactions of various software products by using comprehensive machine learning methods with teams of fellow data scientists with the company. What data scientist and designers in his company like others are doing in the new are of machine learning is upgrading their design systems into systems that uses machine learning by use of artificial intelligence, to have software in items like online shopping, banking and decision recommending predict the user's product preferences, selections, and purchase decisions based on data the machines learn from the users. In this article, Mr. Girardin’s goal is to improve on user experience with design systems his company is responsible for. He believes the new practice of providing the enhanced user satisfaction will beneficial for the customer and the company for 3 reasons. The first is that it will create new types of customer interactions. This means as new technology and software comes out, it will be the"systems designers plus data scientists" jobs to work together to create ways for the machine to learn from the user so that data can be gathered to better understand user preferences for example, movie and music genres. In other words, the more the customer uses the machine more the machine learns. The second reason is the evolution of the relationship between the user and the machine. In this article, Alexa is used as an example, and in its marketing it is used to do everything from turning on the lights to ordering pizza. It has a human and machine helper relationship and this is done by machine learning as the user continues to use the Alexa device. The third reason is in the teamwork relationship between the designers and data scientists." In the field of machine learning, "systems designers plus data scientists will be need to work together to create the system. The designers to create the user friendly interface and the data scientists enable the system to learn from the data it collects. 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Learning Era' $item_summary = 'This article by author Fabien Girardin discusses the duties in his position as a designer at "B.B.V.A. Data and Analytics Company. He assists in the design of systems to provide enhanced user interactions of various software products by using comprehensive machine learning methods with teams of fellow data scientists with the company. What data scientist and designers in his company like others are doing in the new are of machine learning is upgrading their design systems into systems that uses machine learning by use of artificial intelligence, to have software in items like online shopping, banking and decision recommending predict the user's product preferences, selections, and purchase decisions based on data the machines learn from the users. In this article, Mr. Girardin’s goal is to improve on user experience with design systems his company is responsible for. He believes the new practice of providing the enhanced user satisfaction will beneficial for the customer and the company for 3 reasons. The first is that it will create new types of customer interactions. This means as new technology and software comes out, it will be the"systems designers plus data scientists" jobs to work together to create ways for the machine to learn from the user so that data can be gathered to better understand user preferences for example, movie and music genres. In other words, the more the customer uses the machine more the machine learns. The second reason is the evolution of the relationship between the user and the machine. In this article, Alexa is used as an example, and in its marketing it is used to do everything from turning on the lights to ordering pizza. It has a human and machine helper relationship and this is done by machine learning as the user continues to use the Alexa device. The third reason is in the teamwork relationship between the designers and data scientists." In the field of machine learning, "systems designers plus data scientists will be need to work together to create the system. The designers to create the user friendly interface and the data scientists enable the system to learn from the data it collects. In conclusion, the subject of machine learning is clearly explained in this article.' $item_site_name = 'BBVA Data & Analytics' $voice_url = 'https://summarytime.com/uploads/voice_file/7190.MP3' $route_show_url = 'https://summurai.com/'include - APP/View/Article/landing.ctp, line 219 View::_evaluate() - CORE/Cake/View/View.php, line 948 View::_render() - CORE/Cake/View/View.php, line 910 View::render() - CORE/Cake/View/View.php, line 471 Controller::render() - CORE/Cake/Controller/Controller.php, line 954 Dispatcher::_invoke() - CORE/Cake/Routing/Dispatcher.php, line 198 Dispatcher::dispatch() - CORE/Cake/Routing/Dispatcher.php, line 165 [main] - APP/webroot/index.php, line 108
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'title' => 'Experience Design in the Machine Learning Era',
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'description' => 'Traditionally the experience of a digital service follows pre-defined user journeys with clear states and actions. Until recently, it has been the designer’s job to create these linear workflows and transform them into understandable and unobtrusive experiences. This is the story of how that practice is about to change. Over the last 6 months, I have been working in a rather unique position at BBVA Data & Analytics, a center of excellence in financial data analysis. My job is to make the design of user experiences reach a new frontier with the emergence of machine learning techniques. My responsibility — among other things — is to bring a holistic experience design to teams of data scientists and make it an essential part of the lifecycle of algorithmic solutions (e.g. predictive models, recommender systems). In parallel, I perform creative and strategic reviews of experiences that design teams produce (e.g. online banking, online shopping, smart decision making) to steer their evolution into a future of “artificial intelligenceâ€. Practically, I boost the partnerships between teams of designers and data scientists to envision desirable and feasible experiences powered by data and algorithms. Nowadays, the design of many digital services does not only rely on data manipulation and information design but also on systems that learn from their users. If you would open the hood of these systems, you would see that behavioral data (e.g. human interactions, transactions with systems) is fed as context to algorithms that generates knowledge. An interface communicates that knowledge to enrich an experience. Ideally, that experience seeks explicit user actions or implicit sensor events to create a feedback loop that will feed the algorithm with learning material. Discovery Weekly is Spotify’s automated music recommendations “data engine†that brings two hours of custom-made music recommendations, tailored specifically to each Spotify user every Monday. The Discover Weekly’s recommender system leverages the millions playlists that Spotify users create. It gives extra weight to the company’s own experts playlists and those with more followers. The algorithm attempts to augment a person’s listening habits with those with similar tastes. It does it in three main tasks: A typical Discover Weekly playlist recommends 30 songs, a big enough set to discover music that matches with a personal taste among other false positives. That experience provokes the curation of thousands of new playlists that are fed back into the algorithm a week after to generate new recommendations. These feedback loop mechanisms typically offer ways to personalize, optimize or automate existing services. They also create opportunities to design new experiences based on recommendations, predictions or contextualization. At BBVA Data & Analytics I came up with a first non-comprehensive list: We have seen that recommender systems help discover the known unknown or even the unknown unknowns. For instance, Spotify helps discover music through a personalized experience defined on the match between an individual listening behavior and the listening behavior of hundreds of thousands of other individuals. That type of experience has at least three major design challenges. First, recommenders systems have a tendency to create a “filter bubble†that limits suggestions (e.g. products, restaurants, news items, people to connect with) to a world that is strictly linked to a profile built on past behaviors. In response, data scientists must sometimes tweak their algorithms to be less accurate and add a dose of randomness to the suggestions. Second, it is also good design practice to let an open door for users to reshape aspects of their profile that influence the discovery. I would call that feature “profile detoxâ€. Amazon for example allows users to remove items that might negatively influence the recommendations. Imagine the customers purchase gifts for others and those gifts are not necessarily material for future personalized recommendations. Finally, organizations that rely on subjective recommendation like Spotify now enlist humans to give more subjectivity and diversity to the suggested music. This approach of using humans to clean datasets or mitigate the limitations of machine learning algorithm is commonly called “Human Computation†or “Interactive Machine Learningâ€. Data and algorithms also provide means to personalize decision making. For instance at BBVA Data & Analytics we developed advanced techniques to advise BBVA customers on their finance. For example, we consider the temporal evolution of account balances to segment savings behaviors. With that technique we are able to personalize investment opportunities according to each customer’s capacity to save money. This type of algorithms that leads to decision-making needs to learn to be more precise, simply because they often rely on datasets that only give a perspective of reality. In the case of financial advisory, a customer could operate multiple accounts with other banks preventing a clear view on on saving behaviors. It proved a good design practice to let users tell implicitly or explicitly about poor information. It is the data scientist’s responsibility to express the types of feedback that enrich their models and the designer’s job to find ways to make it part of the experience. Traditionally the design of computer programs follows a binary logic with an explicit finite set of concrete and predictable states translated into a workflow. Machine learning algorithms change this with their inherent fuzzy logic. They are designed to look for patterns within a set of sample behaviors to probabilistically approximate the rules of these behaviors (see Machine Learning for Designers for a more detailed introduction to the topic). This approach comes with a certain degree imprecision and unpredictable behaviors. They often return some information on the precision of the information given. For example the booking platform Kayak predicts the evolution of prices according to the analysis of historical prices changes. Its “farecasting†algorithm is designed to return confidence on whether it is a favorable moment to purchase a ticket (see The Machine Learning Behind Farecast). A data scientist is naturally inclined to measure how accurately the algorithm predicts a value: “We predict this fare will be xâ€. That ‘prediction’ is in fact an information based on historical trends. Yet predicting is not the same as informing and a designer must consider how well such a prediction could support a user action: “Buy! this fare is likely to increaseâ€. The ‘likely’ with an overview of the price trend is an example of a “beautiful seam†in the user experience, a notion coined by Mark Weiser at the time of the Xerox Palo Alto Research Center and further developed by Chalmers and MacColl as seamful design: Seamful design is about exploiting failures and limitations to improve the experience. It is about improving the system allowing users to tell about poor recommendations. DJ Patil describes subtle techniques in Data Jujitsu. The ideal for an algorithm is to deliver high precision and recall scores. Unfortunately, precision and recall often work against each other. There is often a need to take design decisions with the trade-off between precision versus recall. For instance, in Spotify Discovery Weekly, a design decision had to be taken to define the size of playlists according to the performance of the recommender system. A large playlist highlights the confidence of Spotify to deliver a rather large inventory of 30 songs, a wide-enough set to increase the opportunities for users to stumble on perfect recommendations. Today, what we read online is based on our own behaviors and the behaviors of other users. Algorithms typically score the relevance of social and news content. The aim of these algorithms is to promote content for higher engagement or send notifications to create habits. Obviously these actions taken on our behalf are not necessarily for our own interest. In the attention economy, both designers and data scientists should learn from the anxieties, obsessions, phobias, stress and other mental burdens of the connected humans. Source: The Global Village and its Discomforts. Photo courtesy of Nicolas Nova. Arguably, we entered into the attention economy, and major online services are fighting to hook people, grap their attention for as long as possible. Their business is to keep users active as long and frequently as possible on their platforms. This leads to the development of sticky, needy experiences that often play with emotions like Fear of Missing Out (FoMO) or other obsessions to dope the user engagement. The actors of the attention economy use also techniques that promote addiction such as Variable Schedule Rewards. It is the exact same mechanisms as the ones used in slot machines. The resulting experience promotes the service’s interest (the casino) hooking people endlessly searching for the next reward. Our mobile phones have become those slot machines of notifications, alerts, messages, retweets, likes, that some of us check on an average 150 times per day if not more. Today designer can use data and algorithms to exploit cognitive vulnerabilities of people in their everyday lives. That new power raises the need for new design principles in the age of machine learning (see The ethics of good design: A principle for the connected age). There are opportunities to design a radically different experience than engagement. Indeed, an organization like a bank has the advantage of being a business that runs on data and does not need customers to spend the maximum amount of time with their services. Tristan Harris’ Time Well Spent movement is particularly inspiring in that sense. He promotes the type of experience that use data to be super-relevant or be silent. The type of technology to protect the user focus and to be respectful of people’s time. The Twitter “While you were away…†is a compelling example of that practice. Other services are good at suggesting moments to engage with them. Instead of measuring user retention, that type of experience focuses on how relevant the interactions are. Data scientist are good in detecting normal behavior and abnormal situations. At BBVA Data & Analytics we are working to promote a peace of mind to BBVA customers with mechanisms that gives a general awareness when things are fine and that trigger more detailed information on abnormal situations. More generally, we believe current generation of machine learning brings new powers to society, but also increases the responsibility of their creators. Algorithmic bias exists and may be inherent to the data sources. In consequence, there is a particular need to make algorithms more legible for people and auditable by regulators to understand their implications. Practically, this means knowledge that the an algorithm produces should safeguard the interest of their users and the results of the evaluation and the criteria used should be explained. In the previous section we have seen that the experiences powered by machine learning are not linear or based on static business and design rules. They evolves according to human behaviors with constantly updating models fed by streams of data. Each product or service becomes almost like a living, breathing thing. Or as people at Google would say: “It’s a different kind of engineeringâ€. I would argue that it is also a different kind of design. For instance, Amazon explains Echo’s braininess as a thing that “continually learns and adds more functionality over timeâ€. This description highlights the need to design the experience for systems to learn from human behavior. Consequently, beyond considering the first contact and the onboarding experience, that type of product or service requires considerations on their use after 1 hour, 1 day, 1 year, etc. If you look at the promotional video of the Edyn garden sensor you will notice the evolution of the experience from creating new habits for taking care of a garden to communicating the unknown unknowns about plants, to convey peace of mind on the key metrics, and to guarantee time well spent with some level of watering automation. That type of data product requires a responsible design that considers moments when things start to disappoint, embarrass, annoy or stop working or being useful. The design of the “offboarding experience†could become almost as important as the “onboarding experienceâ€. For instance, allegedly a third of the Fitbit users stop wearing the device within 6 months. What happens to these millions of abandoned connected objects? What happens to the data and intelligence on the individual they produced? What are the opportunities to use them in different experiences? Products characterized by an experience that evolves according to behavioral data that constantly feed algorithms (e.g. Fitbit) are living products that inevitably also have a tendency to die. Source: The Life and Death of Data Products. There are new ways to imagine the relation after a digital break-up with a product. Digital services work on an increasingly vast ecosystem of things and channels but user data have a tendency to be more centralized. Think about the notion of portable reputation that allows people to use a service based on the relation measured with another service. Looking a bit further into the near future, the recent breakthrough in Natural Language Processing, Knowledge Representation, Voice Recognition and Nature Language Production could create more subtle and stronger relations with machines. In a few iterations, Amazon Echo might start to be much more nurturing. A potential evolution that anthropologist Genevieve Bell foresees a shift from human-computer interactions to human-computer relationships in The next wave of AI is rooted in human culture and history: “So the frame there is not about recommendations, which is where much of AI is now, but is actually about nurture and care. If those become the buzzwords, then you sit in this very interesting moment of being able to pivot from talking about human-computer interactions to human-computer relationships.â€â€Šâ€” Genevieve Bell In this section we have seen that algorithms are getting closer to our everyday lives and that data provide a context for an evolving relationship. The implications of that evolution require most intense collaboration between design and data science. My experience so far envisioning experiences with data and algorithms shows that it is a different practice from current human-centered design. At BBVA Data & Analytics, the role of data scientists has been elevated from reactive model and A/B test developers to proactive partners who think about the implications of their work. Our singular data science teams breaks into sub-teams that partner more directly with engineers, designers, and product managers. At the moment of shaping an experience, we exploit thick data, the qualitative information that provides insights on people’s lives (see Why Big Data Needs Thick Data), big data from the aggregated behavioral data of millions of people and the small data that each individual generates. Classically, designers focus on defining the experience of the service, feature or product. They nest the concept within the larger ecosystem that relates to it. Data scientists develop the algorithms that will support that experience and measure it with A/B testing. The first few weeks in my role at BBVA Data & Analytics, I found designers and data scientists often stuck in deadlocked exchanges that typically sounded like this: The main issue was the lack of shared understanding of each other’s practice and objectives. For instance, designers transform a context into a form of experience. Data scientists transform a context with data and models into knowledge. Designers often adopt a path that adapts to a changing context and new appreciations. Data scientists employ processes similar to humber-center design but are more mechanical and less organic. They strictly follow the scientific methods with its cyclical processes of constant refinement. A properly formulated research question helps define the hypothesis and the types of models to develop in the prototyping phase. The models are the algorithms that get evaluated before they are deployed to production into what we call at BBVA Data & Analytics a “data engineâ€. Whenever the experience supported by the “data engine†does not perform as expected, the problem needs to be reformulated to continue the cyclical process of constant refinement. The scientific method is similar to any design approach that forms and makes new appreciations as new iterations are necessary. Yet, it is not an open-ended process. It has a clear start and end but no definite timeline. Data scientist Neal Lathia argues that “cross-disciplinary work is hard, until you’re speaking the same languageâ€. Additionally, I believe designers and data scientists must immerse themselves in the other’s practice to build a common rhythm. So far, I codified several important touchpoints for designers and data scientists to produce a meaningful user experience powered by algorithms. They must: This intertwined collaboration illustrates a new type of design that I am trying to articulate. In a recent article Harry West CEO at frog suggested the term ‘design of system behavior’: “Human-centered design has expanded from the design of objects (industrial design) to the design of experiences (adding interaction design, visual design, and the design of spaces) and the next step will be the design of system behavior: the design of the algorithms that determine the behavior of automated or intelligent systemsâ€â€Šâ€” Harry West So far I have argued that “living experiences†emerge at the crossroad of data science and design. An indispensable first step is for designers and data scientists is to establish a tangible vision and its outcomes (e.g. experience, solution, priorities, goals, scope and awareness of feasibility). Airbnb Director of Product Jonathan Golden calls that a vision-driven product management approach: “Your company vision is what you want the world to look like in five-plus years — outcomes are the team mandates that will help you get there.†— Jonathan Golden However, that conceptualization phase requires that visions live not just as flat perfect things for board room PowerPoint. Therefore, one of my approaches is to engage the design/science partnership to produce Design Fictions. It has similarities with Amazon’s Working Backward’ process as described by Werner Vogels: “You start with your customer and work your way backwards until you get to the minimum set of technology requirements to satisfy what you try to achieve. The goal is to drive simplicity through a continuous, explicit customer focus.â€â€Šâ€” Werner Vogels Thinking by doing with Design Fiction creates potential futures of a technology to clarify the present. Schema inspired by the Futures Cones and Matt Jones: Jumping to the End — Practical Design Fiction. Design Fiction aims at making tangible the evolution of technologies, the language used to describe them, the rituals, the magic moments, the frustrations, and why not the “offboarding experience”. It helps the different stakeholders of a project to engage with essential questions to understand what the desired experience means and why the team should build it. What are the implications of purchasing that next generation Garden Sensor? What can you do with it? What aren’t you allowed to do? What won’t you do anymore? How does a human interact with that technology the first time, and then routinely after a month, one year or more? Creative and tangible answers to these questions can come to life before a project even starts with the creation of fictional customer reviews, user manual, press release, ads. That material is a way to bring the future to present or as we say at the Near Future Laboratory: “The Design Fictions act as a totem for discussion and evaluation of changes that could bend visions of the desirable and planning of what is necessary.†At BBVA Data & Analytics, this means that I gather data scientists and designers with the objective of creating a tangible vision of their research agenda. First, we first map the ongoing lines of investigations. Then we project their evolution into 2 or 3 iterations wondering: What would the potential resulting technology look like? Where could it be used? Who would use it and for what type of experience? Each participant uses the template of a fictional ad to tell stories with practical answers to these questions. Together we group them into future concepts. We collect all the material and promote the most promising concepts. After that, we share these results internally in series of paper and video advertisements that describe the main features, attributes, characteristics of the experience from our point of view (the feasible) and the user’s point of view (the desirable). This type of fictional material allows both designers and data scientists to feel and get a practical understanding of the technology and its experience. The results help build credibility, enlist support, counter skepticism, create momentum and share a common vision. Finally, the feedback of people with different perspectives allows to anticipate opportunities and challenges. With the advance of machine learning and “artificial intelligence†(AI), it became the responsibility of both designers and data scientists to understand how to shape experiences that improve lives. Or as Greg Borenstein argues in Power to the People: How One Unknown Group of Researchers Holds the Key to Using AI to Solve Real Human Problems: “What’s needed for AI’s wide adoption is an understanding of how to build interfaces that put the power of these systems in the hands of their human users.†— Greg Borenstein That type of design of system behavior represents a future in the tight partnership between design and data science. So far in that journey of creating meaningful experiences in the machine learning era, I can articulate the following characteristics: This is an extended transcript of a talk I gave at the Design Wednesdays event at the BBVA Innovation Center in Madrid on September 21, 2016. Many thanks to the BBVA Design team for their invitation and the quality of the organization!',
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'description' => 'Traditionally the experience of a digital service follows pre-defined user journeys with clear states and actions. Until recently, it has been the designer’s job to create these linear workflows and transform them into understandable and unobtrusive experiences. This is the story of how that practice is about to change. Over the last 6 months, I have been working in a rather unique position at BBVA Data & Analytics, a center of excellence in financial data analysis. My job is to make the design of user experiences reach a new frontier with the emergence of machine learning techniques. My responsibility — among other things — is to bring a holistic experience design to teams of data scientists and make it an essential part of the lifecycle of algorithmic solutions (e.g. predictive models, recommender systems). In parallel, I perform creative and strategic reviews of experiences that design teams produce (e.g. online banking, online shopping, smart decision making) to steer their evolution into a future of “artificial intelligenceâ€. Practically, I boost the partnerships between teams of designers and data scientists to envision desirable and feasible experiences powered by data and algorithms. Nowadays, the design of many digital services does not only rely on data manipulation and information design but also on systems that learn from their users. If you would open the hood of these systems, you would see that behavioral data (e.g. human interactions, transactions with systems) is fed as context to algorithms that generates knowledge. An interface communicates that knowledge to enrich an experience. Ideally, that experience seeks explicit user actions or implicit sensor events to create a feedback loop that will feed the algorithm with learning material. Discovery Weekly is Spotify’s automated music recommendations “data engine†that brings two hours of custom-made music recommendations, tailored specifically to each Spotify user every Monday. The Discover Weekly’s recommender system leverages the millions playlists that Spotify users create. It gives extra weight to the company’s own experts playlists and those with more followers. The algorithm attempts to augment a person’s listening habits with those with similar tastes. It does it in three main tasks: A typical Discover Weekly playlist recommends 30 songs, a big enough set to discover music that matches with a personal taste among other false positives. That experience provokes the curation of thousands of new playlists that are fed back into the algorithm a week after to generate new recommendations. These feedback loop mechanisms typically offer ways to personalize, optimize or automate existing services. They also create opportunities to design new experiences based on recommendations, predictions or contextualization. At BBVA Data & Analytics I came up with a first non-comprehensive list: We have seen that recommender systems help discover the known unknown or even the unknown unknowns. For instance, Spotify helps discover music through a personalized experience defined on the match between an individual listening behavior and the listening behavior of hundreds of thousands of other individuals. That type of experience has at least three major design challenges. First, recommenders systems have a tendency to create a “filter bubble†that limits suggestions (e.g. products, restaurants, news items, people to connect with) to a world that is strictly linked to a profile built on past behaviors. In response, data scientists must sometimes tweak their algorithms to be less accurate and add a dose of randomness to the suggestions. Second, it is also good design practice to let an open door for users to reshape aspects of their profile that influence the discovery. I would call that feature “profile detoxâ€. Amazon for example allows users to remove items that might negatively influence the recommendations. Imagine the customers purchase gifts for others and those gifts are not necessarily material for future personalized recommendations. Finally, organizations that rely on subjective recommendation like Spotify now enlist humans to give more subjectivity and diversity to the suggested music. This approach of using humans to clean datasets or mitigate the limitations of machine learning algorithm is commonly called “Human Computation†or “Interactive Machine Learningâ€. Data and algorithms also provide means to personalize decision making. For instance at BBVA Data & Analytics we developed advanced techniques to advise BBVA customers on their finance. For example, we consider the temporal evolution of account balances to segment savings behaviors. With that technique we are able to personalize investment opportunities according to each customer’s capacity to save money. This type of algorithms that leads to decision-making needs to learn to be more precise, simply because they often rely on datasets that only give a perspective of reality. In the case of financial advisory, a customer could operate multiple accounts with other banks preventing a clear view on on saving behaviors. It proved a good design practice to let users tell implicitly or explicitly about poor information. It is the data scientist’s responsibility to express the types of feedback that enrich their models and the designer’s job to find ways to make it part of the experience. Traditionally the design of computer programs follows a binary logic with an explicit finite set of concrete and predictable states translated into a workflow. Machine learning algorithms change this with their inherent fuzzy logic. They are designed to look for patterns within a set of sample behaviors to probabilistically approximate the rules of these behaviors (see Machine Learning for Designers for a more detailed introduction to the topic). This approach comes with a certain degree imprecision and unpredictable behaviors. They often return some information on the precision of the information given. For example the booking platform Kayak predicts the evolution of prices according to the analysis of historical prices changes. Its “farecasting†algorithm is designed to return confidence on whether it is a favorable moment to purchase a ticket (see The Machine Learning Behind Farecast). A data scientist is naturally inclined to measure how accurately the algorithm predicts a value: “We predict this fare will be xâ€. That ‘prediction’ is in fact an information based on historical trends. Yet predicting is not the same as informing and a designer must consider how well such a prediction could support a user action: “Buy! this fare is likely to increaseâ€. The ‘likely’ with an overview of the price trend is an example of a “beautiful seam†in the user experience, a notion coined by Mark Weiser at the time of the Xerox Palo Alto Research Center and further developed by Chalmers and MacColl as seamful design: Seamful design is about exploiting failures and limitations to improve the experience. It is about improving the system allowing users to tell about poor recommendations. DJ Patil describes subtle techniques in Data Jujitsu. The ideal for an algorithm is to deliver high precision and recall scores. Unfortunately, precision and recall often work against each other. There is often a need to take design decisions with the trade-off between precision versus recall. For instance, in Spotify Discovery Weekly, a design decision had to be taken to define the size of playlists according to the performance of the recommender system. A large playlist highlights the confidence of Spotify to deliver a rather large inventory of 30 songs, a wide-enough set to increase the opportunities for users to stumble on perfect recommendations. Today, what we read online is based on our own behaviors and the behaviors of other users. Algorithms typically score the relevance of social and news content. The aim of these algorithms is to promote content for higher engagement or send notifications to create habits. Obviously these actions taken on our behalf are not necessarily for our own interest. In the attention economy, both designers and data scientists should learn from the anxieties, obsessions, phobias, stress and other mental burdens of the connected humans. Source: The Global Village and its Discomforts. Photo courtesy of Nicolas Nova. Arguably, we entered into the attention economy, and major online services are fighting to hook people, grap their attention for as long as possible. Their business is to keep users active as long and frequently as possible on their platforms. This leads to the development of sticky, needy experiences that often play with emotions like Fear of Missing Out (FoMO) or other obsessions to dope the user engagement. The actors of the attention economy use also techniques that promote addiction such as Variable Schedule Rewards. It is the exact same mechanisms as the ones used in slot machines. The resulting experience promotes the service’s interest (the casino) hooking people endlessly searching for the next reward. Our mobile phones have become those slot machines of notifications, alerts, messages, retweets, likes, that some of us check on an average 150 times per day if not more. Today designer can use data and algorithms to exploit cognitive vulnerabilities of people in their everyday lives. That new power raises the need for new design principles in the age of machine learning (see The ethics of good design: A principle for the connected age). There are opportunities to design a radically different experience than engagement. Indeed, an organization like a bank has the advantage of being a business that runs on data and does not need customers to spend the maximum amount of time with their services. Tristan Harris’ Time Well Spent movement is particularly inspiring in that sense. He promotes the type of experience that use data to be super-relevant or be silent. The type of technology to protect the user focus and to be respectful of people’s time. The Twitter “While you were away…†is a compelling example of that practice. Other services are good at suggesting moments to engage with them. Instead of measuring user retention, that type of experience focuses on how relevant the interactions are. Data scientist are good in detecting normal behavior and abnormal situations. At BBVA Data & Analytics we are working to promote a peace of mind to BBVA customers with mechanisms that gives a general awareness when things are fine and that trigger more detailed information on abnormal situations. More generally, we believe current generation of machine learning brings new powers to society, but also increases the responsibility of their creators. Algorithmic bias exists and may be inherent to the data sources. In consequence, there is a particular need to make algorithms more legible for people and auditable by regulators to understand their implications. Practically, this means knowledge that the an algorithm produces should safeguard the interest of their users and the results of the evaluation and the criteria used should be explained. In the previous section we have seen that the experiences powered by machine learning are not linear or based on static business and design rules. They evolves according to human behaviors with constantly updating models fed by streams of data. Each product or service becomes almost like a living, breathing thing. Or as people at Google would say: “It’s a different kind of engineeringâ€. I would argue that it is also a different kind of design. For instance, Amazon explains Echo’s braininess as a thing that “continually learns and adds more functionality over timeâ€. This description highlights the need to design the experience for systems to learn from human behavior. Consequently, beyond considering the first contact and the onboarding experience, that type of product or service requires considerations on their use after 1 hour, 1 day, 1 year, etc. If you look at the promotional video of the Edyn garden sensor you will notice the evolution of the experience from creating new habits for taking care of a garden to communicating the unknown unknowns about plants, to convey peace of mind on the key metrics, and to guarantee time well spent with some level of watering automation. That type of data product requires a responsible design that considers moments when things start to disappoint, embarrass, annoy or stop working or being useful. The design of the “offboarding experience†could become almost as important as the “onboarding experienceâ€. For instance, allegedly a third of the Fitbit users stop wearing the device within 6 months. What happens to these millions of abandoned connected objects? What happens to the data and intelligence on the individual they produced? What are the opportunities to use them in different experiences? Products characterized by an experience that evolves according to behavioral data that constantly feed algorithms (e.g. Fitbit) are living products that inevitably also have a tendency to die. Source: The Life and Death of Data Products. There are new ways to imagine the relation after a digital break-up with a product. Digital services work on an increasingly vast ecosystem of things and channels but user data have a tendency to be more centralized. Think about the notion of portable reputation that allows people to use a service based on the relation measured with another service. Looking a bit further into the near future, the recent breakthrough in Natural Language Processing, Knowledge Representation, Voice Recognition and Nature Language Production could create more subtle and stronger relations with machines. In a few iterations, Amazon Echo might start to be much more nurturing. A potential evolution that anthropologist Genevieve Bell foresees a shift from human-computer interactions to human-computer relationships in The next wave of AI is rooted in human culture and history: “So the frame there is not about recommendations, which is where much of AI is now, but is actually about nurture and care. If those become the buzzwords, then you sit in this very interesting moment of being able to pivot from talking about human-computer interactions to human-computer relationships.â€â€Šâ€” Genevieve Bell In this section we have seen that algorithms are getting closer to our everyday lives and that data provide a context for an evolving relationship. The implications of that evolution require most intense collaboration between design and data science. My experience so far envisioning experiences with data and algorithms shows that it is a different practice from current human-centered design. At BBVA Data & Analytics, the role of data scientists has been elevated from reactive model and A/B test developers to proactive partners who think about the implications of their work. Our singular data science teams breaks into sub-teams that partner more directly with engineers, designers, and product managers. At the moment of shaping an experience, we exploit thick data, the qualitative information that provides insights on people’s lives (see Why Big Data Needs Thick Data), big data from the aggregated behavioral data of millions of people and the small data that each individual generates. Classically, designers focus on defining the experience of the service, feature or product. They nest the concept within the larger ecosystem that relates to it. Data scientists develop the algorithms that will support that experience and measure it with A/B testing. The first few weeks in my role at BBVA Data & Analytics, I found designers and data scientists often stuck in deadlocked exchanges that typically sounded like this: The main issue was the lack of shared understanding of each other’s practice and objectives. For instance, designers transform a context into a form of experience. Data scientists transform a context with data and models into knowledge. Designers often adopt a path that adapts to a changing context and new appreciations. Data scientists employ processes similar to humber-center design but are more mechanical and less organic. They strictly follow the scientific methods with its cyclical processes of constant refinement. A properly formulated research question helps define the hypothesis and the types of models to develop in the prototyping phase. The models are the algorithms that get evaluated before they are deployed to production into what we call at BBVA Data & Analytics a “data engineâ€. Whenever the experience supported by the “data engine†does not perform as expected, the problem needs to be reformulated to continue the cyclical process of constant refinement. The scientific method is similar to any design approach that forms and makes new appreciations as new iterations are necessary. Yet, it is not an open-ended process. It has a clear start and end but no definite timeline. Data scientist Neal Lathia argues that “cross-disciplinary work is hard, until you’re speaking the same languageâ€. Additionally, I believe designers and data scientists must immerse themselves in the other’s practice to build a common rhythm. So far, I codified several important touchpoints for designers and data scientists to produce a meaningful user experience powered by algorithms. They must: This intertwined collaboration illustrates a new type of design that I am trying to articulate. In a recent article Harry West CEO at frog suggested the term ‘design of system behavior’: “Human-centered design has expanded from the design of objects (industrial design) to the design of experiences (adding interaction design, visual design, and the design of spaces) and the next step will be the design of system behavior: the design of the algorithms that determine the behavior of automated or intelligent systemsâ€â€Šâ€” Harry West So far I have argued that “living experiences†emerge at the crossroad of data science and design. An indispensable first step is for designers and data scientists is to establish a tangible vision and its outcomes (e.g. experience, solution, priorities, goals, scope and awareness of feasibility). Airbnb Director of Product Jonathan Golden calls that a vision-driven product management approach: “Your company vision is what you want the world to look like in five-plus years — outcomes are the team mandates that will help you get there.†— Jonathan Golden However, that conceptualization phase requires that visions live not just as flat perfect things for board room PowerPoint. Therefore, one of my approaches is to engage the design/science partnership to produce Design Fictions. It has similarities with Amazon’s Working Backward’ process as described by Werner Vogels: “You start with your customer and work your way backwards until you get to the minimum set of technology requirements to satisfy what you try to achieve. The goal is to drive simplicity through a continuous, explicit customer focus.â€â€Šâ€” Werner Vogels Thinking by doing with Design Fiction creates potential futures of a technology to clarify the present. Schema inspired by the Futures Cones and Matt Jones: Jumping to the End — Practical Design Fiction. Design Fiction aims at making tangible the evolution of technologies, the language used to describe them, the rituals, the magic moments, the frustrations, and why not the “offboarding experience”. It helps the different stakeholders of a project to engage with essential questions to understand what the desired experience means and why the team should build it. What are the implications of purchasing that next generation Garden Sensor? What can you do with it? What aren’t you allowed to do? What won’t you do anymore? How does a human interact with that technology the first time, and then routinely after a month, one year or more? Creative and tangible answers to these questions can come to life before a project even starts with the creation of fictional customer reviews, user manual, press release, ads. That material is a way to bring the future to present or as we say at the Near Future Laboratory: “The Design Fictions act as a totem for discussion and evaluation of changes that could bend visions of the desirable and planning of what is necessary.†At BBVA Data & Analytics, this means that I gather data scientists and designers with the objective of creating a tangible vision of their research agenda. First, we first map the ongoing lines of investigations. Then we project their evolution into 2 or 3 iterations wondering: What would the potential resulting technology look like? Where could it be used? Who would use it and for what type of experience? Each participant uses the template of a fictional ad to tell stories with practical answers to these questions. Together we group them into future concepts. We collect all the material and promote the most promising concepts. After that, we share these results internally in series of paper and video advertisements that describe the main features, attributes, characteristics of the experience from our point of view (the feasible) and the user’s point of view (the desirable). This type of fictional material allows both designers and data scientists to feel and get a practical understanding of the technology and its experience. The results help build credibility, enlist support, counter skepticism, create momentum and share a common vision. Finally, the feedback of people with different perspectives allows to anticipate opportunities and challenges. With the advance of machine learning and “artificial intelligence†(AI), it became the responsibility of both designers and data scientists to understand how to shape experiences that improve lives. Or as Greg Borenstein argues in Power to the People: How One Unknown Group of Researchers Holds the Key to Using AI to Solve Real Human Problems: “What’s needed for AI’s wide adoption is an understanding of how to build interfaces that put the power of these systems in the hands of their human users.†— Greg Borenstein That type of design of system behavior represents a future in the tight partnership between design and data science. So far in that journey of creating meaningful experiences in the machine learning era, I can articulate the following characteristics: This is an extended transcript of a talk I gave at the Design Wednesdays event at the BBVA Innovation Center in Madrid on September 21, 2016. Many thanks to the BBVA Design team for their invitation and the quality of the organization!',
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$item_summary = 'This article by author Fabien Girardin discusses the duties in his position as a designer at "B.B.V.A. Data and Analytics Company. He assists in the design of systems to provide enhanced user interactions of various software products by using comprehensive machine learning methods with teams of fellow data scientists with the company. What data scientist and designers in his company like others are doing in the new are of machine learning is upgrading their design systems into systems that uses machine learning by use of artificial intelligence, to have software in items like online shopping, banking and decision recommending predict the user's product preferences, selections, and purchase decisions based on data the machines learn from the users. In this article, Mr. Girardin’s goal is to improve on user experience with design systems his company is responsible for. He believes the new practice of providing the enhanced user satisfaction will beneficial for the customer and the company for 3 reasons. The first is that it will create new types of customer interactions. This means as new technology and software comes out, it will be the"systems designers plus data scientists" jobs to work together to create ways for the machine to learn from the user so that data can be gathered to better understand user preferences for example, movie and music genres. In other words, the more the customer uses the machine more the machine learns. The second reason is the evolution of the relationship between the user and the machine. In this article, Alexa is used as an example, and in its marketing it is used to do everything from turning on the lights to ordering pizza. It has a human and machine helper relationship and this is done by machine learning as the user continues to use the Alexa device. The third reason is in the teamwork relationship between the designers and data scientists." In the field of machine learning, "systems designers plus data scientists will be need to work together to create the system. The designers to create the user friendly interface and the data scientists enable the system to learn from the data it collects. In conclusion, the subject of machine learning is clearly explained in this article.'
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We're on it.
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<div class="thumbs-up-part stap02 help_desk_success" style="display:none;"><div class="thumbs-icon"><?php echo $this->Html->image(($brand_details['Client']['pseudo_language_id']==2?'images/thumbs-up-icon.jpg':'images/thumbs-up-icon-eng.png'), array('alt'=>'','class'=>''));?></div><h2><?php echo (($brand_details['Client']['pseudo_language_id']==2)?'אנחנו על זה.':"We're on it.");?></h2>
$viewFile = '/home/summarytime/summurai.com/app/View/Article/landing.ctp' $dataForView = array( 'data' => array( 'MyItem' => array( 'id' => '7190', 'user_master_id' => '188', 'guid' => null, 'posted_by' => '332', 'voice_by' => '1561', 'post_market_id' => '5399', 'image_url' => 'http://www.bbvadata.com/wp-content/uploads/2016/12/discover-weekly-ml.jpg', 'title' => 'Experience Design in the Machine Learning Era', 'other_title' => '', 'description' => 'Traditionally the experience of a digital service follows pre-defined user journeys with clear states and actions. Until recently, it has been the designer’s job to create these linear workflows and transform them into understandable and unobtrusive experiences. This is the story of how that practice is about to change. Over the last 6 months, I have been working in a rather unique position at BBVA Data & Analytics, a center of excellence in financial data analysis. My job is to make the design of user experiences reach a new frontier with the emergence of machine learning techniques. My responsibility — among other things — is to bring a holistic experience design to teams of data scientists and make it an essential part of the lifecycle of algorithmic solutions (e.g. predictive models, recommender systems). In parallel, I perform creative and strategic reviews of experiences that design teams produce (e.g. online banking, online shopping, smart decision making) to steer their evolution into a future of “artificial intelligenceâ€. Practically, I boost the partnerships between teams of designers and data scientists to envision desirable and feasible experiences powered by data and algorithms. Nowadays, the design of many digital services does not only rely on data manipulation and information design but also on systems that learn from their users. If you would open the hood of these systems, you would see that behavioral data (e.g. human interactions, transactions with systems) is fed as context to algorithms that generates knowledge. An interface communicates that knowledge to enrich an experience. Ideally, that experience seeks explicit user actions or implicit sensor events to create a feedback loop that will feed the algorithm with learning material. Discovery Weekly is Spotify’s automated music recommendations “data engine†that brings two hours of custom-made music recommendations, tailored specifically to each Spotify user every Monday. The Discover Weekly’s recommender system leverages the millions playlists that Spotify users create. It gives extra weight to the company’s own experts playlists and those with more followers. The algorithm attempts to augment a person’s listening habits with those with similar tastes. It does it in three main tasks: A typical Discover Weekly playlist recommends 30 songs, a big enough set to discover music that matches with a personal taste among other false positives. That experience provokes the curation of thousands of new playlists that are fed back into the algorithm a week after to generate new recommendations. These feedback loop mechanisms typically offer ways to personalize, optimize or automate existing services. They also create opportunities to design new experiences based on recommendations, predictions or contextualization. At BBVA Data & Analytics I came up with a first non-comprehensive list: We have seen that recommender systems help discover the known unknown or even the unknown unknowns. For instance, Spotify helps discover music through a personalized experience defined on the match between an individual listening behavior and the listening behavior of hundreds of thousands of other individuals. That type of experience has at least three major design challenges. First, recommenders systems have a tendency to create a “filter bubble†that limits suggestions (e.g. products, restaurants, news items, people to connect with) to a world that is strictly linked to a profile built on past behaviors. In response, data scientists must sometimes tweak their algorithms to be less accurate and add a dose of randomness to the suggestions. Second, it is also good design practice to let an open door for users to reshape aspects of their profile that influence the discovery. I would call that feature “profile detoxâ€. Amazon for example allows users to remove items that might negatively influence the recommendations. Imagine the customers purchase gifts for others and those gifts are not necessarily material for future personalized recommendations. Finally, organizations that rely on subjective recommendation like Spotify now enlist humans to give more subjectivity and diversity to the suggested music. This approach of using humans to clean datasets or mitigate the limitations of machine learning algorithm is commonly called “Human Computation†or “Interactive Machine Learningâ€. Data and algorithms also provide means to personalize decision making. For instance at BBVA Data & Analytics we developed advanced techniques to advise BBVA customers on their finance. For example, we consider the temporal evolution of account balances to segment savings behaviors. With that technique we are able to personalize investment opportunities according to each customer’s capacity to save money. This type of algorithms that leads to decision-making needs to learn to be more precise, simply because they often rely on datasets that only give a perspective of reality. In the case of financial advisory, a customer could operate multiple accounts with other banks preventing a clear view on on saving behaviors. It proved a good design practice to let users tell implicitly or explicitly about poor information. It is the data scientist’s responsibility to express the types of feedback that enrich their models and the designer’s job to find ways to make it part of the experience. Traditionally the design of computer programs follows a binary logic with an explicit finite set of concrete and predictable states translated into a workflow. Machine learning algorithms change this with their inherent fuzzy logic. They are designed to look for patterns within a set of sample behaviors to probabilistically approximate the rules of these behaviors (see Machine Learning for Designers for a more detailed introduction to the topic). This approach comes with a certain degree imprecision and unpredictable behaviors. They often return some information on the precision of the information given. For example the booking platform Kayak predicts the evolution of prices according to the analysis of historical prices changes. Its “farecasting†algorithm is designed to return confidence on whether it is a favorable moment to purchase a ticket (see The Machine Learning Behind Farecast). A data scientist is naturally inclined to measure how accurately the algorithm predicts a value: “We predict this fare will be xâ€. That ‘prediction’ is in fact an information based on historical trends. Yet predicting is not the same as informing and a designer must consider how well such a prediction could support a user action: “Buy! this fare is likely to increaseâ€. The ‘likely’ with an overview of the price trend is an example of a “beautiful seam†in the user experience, a notion coined by Mark Weiser at the time of the Xerox Palo Alto Research Center and further developed by Chalmers and MacColl as seamful design: Seamful design is about exploiting failures and limitations to improve the experience. It is about improving the system allowing users to tell about poor recommendations. DJ Patil describes subtle techniques in Data Jujitsu. The ideal for an algorithm is to deliver high precision and recall scores. Unfortunately, precision and recall often work against each other. There is often a need to take design decisions with the trade-off between precision versus recall. For instance, in Spotify Discovery Weekly, a design decision had to be taken to define the size of playlists according to the performance of the recommender system. A large playlist highlights the confidence of Spotify to deliver a rather large inventory of 30 songs, a wide-enough set to increase the opportunities for users to stumble on perfect recommendations. Today, what we read online is based on our own behaviors and the behaviors of other users. Algorithms typically score the relevance of social and news content. The aim of these algorithms is to promote content for higher engagement or send notifications to create habits. Obviously these actions taken on our behalf are not necessarily for our own interest. In the attention economy, both designers and data scientists should learn from the anxieties, obsessions, phobias, stress and other mental burdens of the connected humans. Source: The Global Village and its Discomforts. Photo courtesy of Nicolas Nova. Arguably, we entered into the attention economy, and major online services are fighting to hook people, grap their attention for as long as possible. Their business is to keep users active as long and frequently as possible on their platforms. This leads to the development of sticky, needy experiences that often play with emotions like Fear of Missing Out (FoMO) or other obsessions to dope the user engagement. The actors of the attention economy use also techniques that promote addiction such as Variable Schedule Rewards. It is the exact same mechanisms as the ones used in slot machines. The resulting experience promotes the service’s interest (the casino) hooking people endlessly searching for the next reward. Our mobile phones have become those slot machines of notifications, alerts, messages, retweets, likes, that some of us check on an average 150 times per day if not more. Today designer can use data and algorithms to exploit cognitive vulnerabilities of people in their everyday lives. That new power raises the need for new design principles in the age of machine learning (see The ethics of good design: A principle for the connected age). There are opportunities to design a radically different experience than engagement. Indeed, an organization like a bank has the advantage of being a business that runs on data and does not need customers to spend the maximum amount of time with their services. Tristan Harris’ Time Well Spent movement is particularly inspiring in that sense. He promotes the type of experience that use data to be super-relevant or be silent. The type of technology to protect the user focus and to be respectful of people’s time. The Twitter “While you were away…†is a compelling example of that practice. Other services are good at suggesting moments to engage with them. Instead of measuring user retention, that type of experience focuses on how relevant the interactions are. Data scientist are good in detecting normal behavior and abnormal situations. At BBVA Data & Analytics we are working to promote a peace of mind to BBVA customers with mechanisms that gives a general awareness when things are fine and that trigger more detailed information on abnormal situations. More generally, we believe current generation of machine learning brings new powers to society, but also increases the responsibility of their creators. Algorithmic bias exists and may be inherent to the data sources. In consequence, there is a particular need to make algorithms more legible for people and auditable by regulators to understand their implications. Practically, this means knowledge that the an algorithm produces should safeguard the interest of their users and the results of the evaluation and the criteria used should be explained. In the previous section we have seen that the experiences powered by machine learning are not linear or based on static business and design rules. They evolves according to human behaviors with constantly updating models fed by streams of data. Each product or service becomes almost like a living, breathing thing. Or as people at Google would say: “It’s a different kind of engineeringâ€. I would argue that it is also a different kind of design. For instance, Amazon explains Echo’s braininess as a thing that “continually learns and adds more functionality over timeâ€. This description highlights the need to design the experience for systems to learn from human behavior. Consequently, beyond considering the first contact and the onboarding experience, that type of product or service requires considerations on their use after 1 hour, 1 day, 1 year, etc. If you look at the promotional video of the Edyn garden sensor you will notice the evolution of the experience from creating new habits for taking care of a garden to communicating the unknown unknowns about plants, to convey peace of mind on the key metrics, and to guarantee time well spent with some level of watering automation. That type of data product requires a responsible design that considers moments when things start to disappoint, embarrass, annoy or stop working or being useful. The design of the “offboarding experience†could become almost as important as the “onboarding experienceâ€. For instance, allegedly a third of the Fitbit users stop wearing the device within 6 months. What happens to these millions of abandoned connected objects? What happens to the data and intelligence on the individual they produced? What are the opportunities to use them in different experiences? Products characterized by an experience that evolves according to behavioral data that constantly feed algorithms (e.g. Fitbit) are living products that inevitably also have a tendency to die. Source: The Life and Death of Data Products. There are new ways to imagine the relation after a digital break-up with a product. Digital services work on an increasingly vast ecosystem of things and channels but user data have a tendency to be more centralized. Think about the notion of portable reputation that allows people to use a service based on the relation measured with another service. Looking a bit further into the near future, the recent breakthrough in Natural Language Processing, Knowledge Representation, Voice Recognition and Nature Language Production could create more subtle and stronger relations with machines. In a few iterations, Amazon Echo might start to be much more nurturing. A potential evolution that anthropologist Genevieve Bell foresees a shift from human-computer interactions to human-computer relationships in The next wave of AI is rooted in human culture and history: “So the frame there is not about recommendations, which is where much of AI is now, but is actually about nurture and care. If those become the buzzwords, then you sit in this very interesting moment of being able to pivot from talking about human-computer interactions to human-computer relationships.â€â€Šâ€” Genevieve Bell In this section we have seen that algorithms are getting closer to our everyday lives and that data provide a context for an evolving relationship. The implications of that evolution require most intense collaboration between design and data science. My experience so far envisioning experiences with data and algorithms shows that it is a different practice from current human-centered design. At BBVA Data & Analytics, the role of data scientists has been elevated from reactive model and A/B test developers to proactive partners who think about the implications of their work. Our singular data science teams breaks into sub-teams that partner more directly with engineers, designers, and product managers. At the moment of shaping an experience, we exploit thick data, the qualitative information that provides insights on people’s lives (see Why Big Data Needs Thick Data), big data from the aggregated behavioral data of millions of people and the small data that each individual generates. Classically, designers focus on defining the experience of the service, feature or product. They nest the concept within the larger ecosystem that relates to it. Data scientists develop the algorithms that will support that experience and measure it with A/B testing. The first few weeks in my role at BBVA Data & Analytics, I found designers and data scientists often stuck in deadlocked exchanges that typically sounded like this: The main issue was the lack of shared understanding of each other’s practice and objectives. For instance, designers transform a context into a form of experience. Data scientists transform a context with data and models into knowledge. Designers often adopt a path that adapts to a changing context and new appreciations. Data scientists employ processes similar to humber-center design but are more mechanical and less organic. They strictly follow the scientific methods with its cyclical processes of constant refinement. A properly formulated research question helps define the hypothesis and the types of models to develop in the prototyping phase. The models are the algorithms that get evaluated before they are deployed to production into what we call at BBVA Data & Analytics a “data engineâ€. Whenever the experience supported by the “data engine†does not perform as expected, the problem needs to be reformulated to continue the cyclical process of constant refinement. The scientific method is similar to any design approach that forms and makes new appreciations as new iterations are necessary. Yet, it is not an open-ended process. It has a clear start and end but no definite timeline. Data scientist Neal Lathia argues that “cross-disciplinary work is hard, until you’re speaking the same languageâ€. Additionally, I believe designers and data scientists must immerse themselves in the other’s practice to build a common rhythm. So far, I codified several important touchpoints for designers and data scientists to produce a meaningful user experience powered by algorithms. They must: This intertwined collaboration illustrates a new type of design that I am trying to articulate. In a recent article Harry West CEO at frog suggested the term ‘design of system behavior’: “Human-centered design has expanded from the design of objects (industrial design) to the design of experiences (adding interaction design, visual design, and the design of spaces) and the next step will be the design of system behavior: the design of the algorithms that determine the behavior of automated or intelligent systemsâ€â€Šâ€” Harry West So far I have argued that “living experiences†emerge at the crossroad of data science and design. An indispensable first step is for designers and data scientists is to establish a tangible vision and its outcomes (e.g. experience, solution, priorities, goals, scope and awareness of feasibility). Airbnb Director of Product Jonathan Golden calls that a vision-driven product management approach: “Your company vision is what you want the world to look like in five-plus years — outcomes are the team mandates that will help you get there.†— Jonathan Golden However, that conceptualization phase requires that visions live not just as flat perfect things for board room PowerPoint. Therefore, one of my approaches is to engage the design/science partnership to produce Design Fictions. It has similarities with Amazon’s Working Backward’ process as described by Werner Vogels: “You start with your customer and work your way backwards until you get to the minimum set of technology requirements to satisfy what you try to achieve. The goal is to drive simplicity through a continuous, explicit customer focus.â€â€Šâ€” Werner Vogels Thinking by doing with Design Fiction creates potential futures of a technology to clarify the present. Schema inspired by the Futures Cones and Matt Jones: Jumping to the End — Practical Design Fiction. Design Fiction aims at making tangible the evolution of technologies, the language used to describe them, the rituals, the magic moments, the frustrations, and why not the “offboarding experience”. It helps the different stakeholders of a project to engage with essential questions to understand what the desired experience means and why the team should build it. What are the implications of purchasing that next generation Garden Sensor? What can you do with it? What aren’t you allowed to do? What won’t you do anymore? How does a human interact with that technology the first time, and then routinely after a month, one year or more? Creative and tangible answers to these questions can come to life before a project even starts with the creation of fictional customer reviews, user manual, press release, ads. That material is a way to bring the future to present or as we say at the Near Future Laboratory: “The Design Fictions act as a totem for discussion and evaluation of changes that could bend visions of the desirable and planning of what is necessary.†At BBVA Data & Analytics, this means that I gather data scientists and designers with the objective of creating a tangible vision of their research agenda. First, we first map the ongoing lines of investigations. Then we project their evolution into 2 or 3 iterations wondering: What would the potential resulting technology look like? Where could it be used? Who would use it and for what type of experience? Each participant uses the template of a fictional ad to tell stories with practical answers to these questions. Together we group them into future concepts. We collect all the material and promote the most promising concepts. After that, we share these results internally in series of paper and video advertisements that describe the main features, attributes, characteristics of the experience from our point of view (the feasible) and the user’s point of view (the desirable). This type of fictional material allows both designers and data scientists to feel and get a practical understanding of the technology and its experience. The results help build credibility, enlist support, counter skepticism, create momentum and share a common vision. Finally, the feedback of people with different perspectives allows to anticipate opportunities and challenges. With the advance of machine learning and “artificial intelligence†(AI), it became the responsibility of both designers and data scientists to understand how to shape experiences that improve lives. Or as Greg Borenstein argues in Power to the People: How One Unknown Group of Researchers Holds the Key to Using AI to Solve Real Human Problems: “What’s needed for AI’s wide adoption is an understanding of how to build interfaces that put the power of these systems in the hands of their human users.†— Greg Borenstein That type of design of system behavior represents a future in the tight partnership between design and data science. So far in that journey of creating meaningful experiences in the machine learning era, I can articulate the following characteristics: This is an extended transcript of a talk I gave at the Design Wednesdays event at the BBVA Innovation Center in Madrid on September 21, 2016. Many thanks to the BBVA Design team for their invitation and the quality of the organization!', 'summary' => '<p>This article by author Fabien Girardin discusses the duties in his position as a designer at "B.B.V.A. Data and Analytics Company. He assists in the design of systems to provide enhanced user interactions of various software products by using comprehensive machine learning methods with teams of fellow data scientists with the company. What data scientist and designers in his company like others are doing in the new are of machine learning is upgrading their design systems into systems that uses machine learning by use of artificial intelligence, to have software in items like online shopping, banking and decision recommending predict the user's product preferences, selections, and purchase decisions based on data the machines learn from the users. In this article, Mr. Girardin’s goal is to improve on user experience with design systems his company is responsible for. He believes the new practice of providing the enhanced user satisfaction will beneficial for the customer and the company for 3 reasons. The first is that it will create new types of customer interactions. This means as new technology and software comes out, it will be the"systems designers plus data scientists" jobs to work together to create ways for the machine to learn from the user so that data can be gathered to better understand user preferences for example, movie and music genres. In other words, the more the customer uses the machine more the machine learns. The second reason is the evolution of the relationship between the user and the machine. In this article, Alexa is used as an example, and in its marketing it is used to do everything from turning on the lights to ordering pizza. It has a human and machine helper relationship and this is done by machine learning as the user continues to use the Alexa device. The third reason is in the teamwork relationship between the designers and data scientists." In the field of machine learning, "systems designers plus data scientists will be need to work together to create the system. The designers to create the user friendly interface and the data scientists enable the system to learn from the data it collects. 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Until recently, it has been the designer’s job to create these linear workflows and transform them into understandable and unobtrusive experiences. This is the story of how that practice is about to change. Over the last 6 months, I have been working in a rather unique position at BBVA Data & Analytics, a center of excellence in financial data analysis. My job is to make the design of user experiences reach a new frontier with the emergence of machine learning techniques. My responsibility — among other things — is to bring a holistic experience design to teams of data scientists and make it an essential part of the lifecycle of algorithmic solutions (e.g. predictive models, recommender systems). In parallel, I perform creative and strategic reviews of experiences that design teams produce (e.g. online banking, online shopping, smart decision making) to steer their evolution into a future of “artificial intelligenceâ€. Practically, I boost the partnerships between teams of designers and data scientists to envision desirable and feasible experiences powered by data and algorithms. Nowadays, the design of many digital services does not only rely on data manipulation and information design but also on systems that learn from their users. If you would open the hood of these systems, you would see that behavioral data (e.g. human interactions, transactions with systems) is fed as context to algorithms that generates knowledge. An interface communicates that knowledge to enrich an experience. Ideally, that experience seeks explicit user actions or implicit sensor events to create a feedback loop that will feed the algorithm with learning material. Discovery Weekly is Spotify’s automated music recommendations “data engine†that brings two hours of custom-made music recommendations, tailored specifically to each Spotify user every Monday. The Discover Weekly’s recommender system leverages the millions playlists that Spotify users create. It gives extra weight to the company’s own experts playlists and those with more followers. The algorithm attempts to augment a person’s listening habits with those with similar tastes. It does it in three main tasks: A typical Discover Weekly playlist recommends 30 songs, a big enough set to discover music that matches with a personal taste among other false positives. That experience provokes the curation of thousands of new playlists that are fed back into the algorithm a week after to generate new recommendations. These feedback loop mechanisms typically offer ways to personalize, optimize or automate existing services. They also create opportunities to design new experiences based on recommendations, predictions or contextualization. At BBVA Data & Analytics I came up with a first non-comprehensive list: We have seen that recommender systems help discover the known unknown or even the unknown unknowns. For instance, Spotify helps discover music through a personalized experience defined on the match between an individual listening behavior and the listening behavior of hundreds of thousands of other individuals. That type of experience has at least three major design challenges. First, recommenders systems have a tendency to create a “filter bubble†that limits suggestions (e.g. products, restaurants, news items, people to connect with) to a world that is strictly linked to a profile built on past behaviors. In response, data scientists must sometimes tweak their algorithms to be less accurate and add a dose of randomness to the suggestions. Second, it is also good design practice to let an open door for users to reshape aspects of their profile that influence the discovery. I would call that feature “profile detoxâ€. Amazon for example allows users to remove items that might negatively influence the recommendations. Imagine the customers purchase gifts for others and those gifts are not necessarily material for future personalized recommendations. Finally, organizations that rely on subjective recommendation like Spotify now enlist humans to give more subjectivity and diversity to the suggested music. This approach of using humans to clean datasets or mitigate the limitations of machine learning algorithm is commonly called “Human Computation†or “Interactive Machine Learningâ€. Data and algorithms also provide means to personalize decision making. For instance at BBVA Data & Analytics we developed advanced techniques to advise BBVA customers on their finance. For example, we consider the temporal evolution of account balances to segment savings behaviors. With that technique we are able to personalize investment opportunities according to each customer’s capacity to save money. This type of algorithms that leads to decision-making needs to learn to be more precise, simply because they often rely on datasets that only give a perspective of reality. In the case of financial advisory, a customer could operate multiple accounts with other banks preventing a clear view on on saving behaviors. It proved a good design practice to let users tell implicitly or explicitly about poor information. It is the data scientist’s responsibility to express the types of feedback that enrich their models and the designer’s job to find ways to make it part of the experience. Traditionally the design of computer programs follows a binary logic with an explicit finite set of concrete and predictable states translated into a workflow. Machine learning algorithms change this with their inherent fuzzy logic. They are designed to look for patterns within a set of sample behaviors to probabilistically approximate the rules of these behaviors (see Machine Learning for Designers for a more detailed introduction to the topic). This approach comes with a certain degree imprecision and unpredictable behaviors. They often return some information on the precision of the information given. For example the booking platform Kayak predicts the evolution of prices according to the analysis of historical prices changes. Its “farecasting†algorithm is designed to return confidence on whether it is a favorable moment to purchase a ticket (see The Machine Learning Behind Farecast). A data scientist is naturally inclined to measure how accurately the algorithm predicts a value: “We predict this fare will be xâ€. That ‘prediction’ is in fact an information based on historical trends. Yet predicting is not the same as informing and a designer must consider how well such a prediction could support a user action: “Buy! this fare is likely to increaseâ€. The ‘likely’ with an overview of the price trend is an example of a “beautiful seam†in the user experience, a notion coined by Mark Weiser at the time of the Xerox Palo Alto Research Center and further developed by Chalmers and MacColl as seamful design: Seamful design is about exploiting failures and limitations to improve the experience. It is about improving the system allowing users to tell about poor recommendations. DJ Patil describes subtle techniques in Data Jujitsu. The ideal for an algorithm is to deliver high precision and recall scores. Unfortunately, precision and recall often work against each other. There is often a need to take design decisions with the trade-off between precision versus recall. For instance, in Spotify Discovery Weekly, a design decision had to be taken to define the size of playlists according to the performance of the recommender system. A large playlist highlights the confidence of Spotify to deliver a rather large inventory of 30 songs, a wide-enough set to increase the opportunities for users to stumble on perfect recommendations. Today, what we read online is based on our own behaviors and the behaviors of other users. Algorithms typically score the relevance of social and news content. The aim of these algorithms is to promote content for higher engagement or send notifications to create habits. Obviously these actions taken on our behalf are not necessarily for our own interest. In the attention economy, both designers and data scientists should learn from the anxieties, obsessions, phobias, stress and other mental burdens of the connected humans. Source: The Global Village and its Discomforts. Photo courtesy of Nicolas Nova. Arguably, we entered into the attention economy, and major online services are fighting to hook people, grap their attention for as long as possible. Their business is to keep users active as long and frequently as possible on their platforms. This leads to the development of sticky, needy experiences that often play with emotions like Fear of Missing Out (FoMO) or other obsessions to dope the user engagement. The actors of the attention economy use also techniques that promote addiction such as Variable Schedule Rewards. It is the exact same mechanisms as the ones used in slot machines. The resulting experience promotes the service’s interest (the casino) hooking people endlessly searching for the next reward. Our mobile phones have become those slot machines of notifications, alerts, messages, retweets, likes, that some of us check on an average 150 times per day if not more. Today designer can use data and algorithms to exploit cognitive vulnerabilities of people in their everyday lives. That new power raises the need for new design principles in the age of machine learning (see The ethics of good design: A principle for the connected age). There are opportunities to design a radically different experience than engagement. Indeed, an organization like a bank has the advantage of being a business that runs on data and does not need customers to spend the maximum amount of time with their services. Tristan Harris’ Time Well Spent movement is particularly inspiring in that sense. He promotes the type of experience that use data to be super-relevant or be silent. The type of technology to protect the user focus and to be respectful of people’s time. The Twitter “While you were away…†is a compelling example of that practice. Other services are good at suggesting moments to engage with them. Instead of measuring user retention, that type of experience focuses on how relevant the interactions are. Data scientist are good in detecting normal behavior and abnormal situations. At BBVA Data & Analytics we are working to promote a peace of mind to BBVA customers with mechanisms that gives a general awareness when things are fine and that trigger more detailed information on abnormal situations. More generally, we believe current generation of machine learning brings new powers to society, but also increases the responsibility of their creators. Algorithmic bias exists and may be inherent to the data sources. In consequence, there is a particular need to make algorithms more legible for people and auditable by regulators to understand their implications. Practically, this means knowledge that the an algorithm produces should safeguard the interest of their users and the results of the evaluation and the criteria used should be explained. In the previous section we have seen that the experiences powered by machine learning are not linear or based on static business and design rules. They evolves according to human behaviors with constantly updating models fed by streams of data. Each product or service becomes almost like a living, breathing thing. Or as people at Google would say: “It’s a different kind of engineeringâ€. I would argue that it is also a different kind of design. For instance, Amazon explains Echo’s braininess as a thing that “continually learns and adds more functionality over timeâ€. This description highlights the need to design the experience for systems to learn from human behavior. Consequently, beyond considering the first contact and the onboarding experience, that type of product or service requires considerations on their use after 1 hour, 1 day, 1 year, etc. If you look at the promotional video of the Edyn garden sensor you will notice the evolution of the experience from creating new habits for taking care of a garden to communicating the unknown unknowns about plants, to convey peace of mind on the key metrics, and to guarantee time well spent with some level of watering automation. That type of data product requires a responsible design that considers moments when things start to disappoint, embarrass, annoy or stop working or being useful. The design of the “offboarding experience†could become almost as important as the “onboarding experienceâ€. For instance, allegedly a third of the Fitbit users stop wearing the device within 6 months. What happens to these millions of abandoned connected objects? What happens to the data and intelligence on the individual they produced? What are the opportunities to use them in different experiences? Products characterized by an experience that evolves according to behavioral data that constantly feed algorithms (e.g. Fitbit) are living products that inevitably also have a tendency to die. Source: The Life and Death of Data Products. There are new ways to imagine the relation after a digital break-up with a product. Digital services work on an increasingly vast ecosystem of things and channels but user data have a tendency to be more centralized. Think about the notion of portable reputation that allows people to use a service based on the relation measured with another service. Looking a bit further into the near future, the recent breakthrough in Natural Language Processing, Knowledge Representation, Voice Recognition and Nature Language Production could create more subtle and stronger relations with machines. In a few iterations, Amazon Echo might start to be much more nurturing. A potential evolution that anthropologist Genevieve Bell foresees a shift from human-computer interactions to human-computer relationships in The next wave of AI is rooted in human culture and history: “So the frame there is not about recommendations, which is where much of AI is now, but is actually about nurture and care. If those become the buzzwords, then you sit in this very interesting moment of being able to pivot from talking about human-computer interactions to human-computer relationships.â€â€Šâ€” Genevieve Bell In this section we have seen that algorithms are getting closer to our everyday lives and that data provide a context for an evolving relationship. The implications of that evolution require most intense collaboration between design and data science. My experience so far envisioning experiences with data and algorithms shows that it is a different practice from current human-centered design. At BBVA Data & Analytics, the role of data scientists has been elevated from reactive model and A/B test developers to proactive partners who think about the implications of their work. Our singular data science teams breaks into sub-teams that partner more directly with engineers, designers, and product managers. At the moment of shaping an experience, we exploit thick data, the qualitative information that provides insights on people’s lives (see Why Big Data Needs Thick Data), big data from the aggregated behavioral data of millions of people and the small data that each individual generates. Classically, designers focus on defining the experience of the service, feature or product. They nest the concept within the larger ecosystem that relates to it. Data scientists develop the algorithms that will support that experience and measure it with A/B testing. The first few weeks in my role at BBVA Data & Analytics, I found designers and data scientists often stuck in deadlocked exchanges that typically sounded like this: The main issue was the lack of shared understanding of each other’s practice and objectives. For instance, designers transform a context into a form of experience. Data scientists transform a context with data and models into knowledge. Designers often adopt a path that adapts to a changing context and new appreciations. Data scientists employ processes similar to humber-center design but are more mechanical and less organic. They strictly follow the scientific methods with its cyclical processes of constant refinement. A properly formulated research question helps define the hypothesis and the types of models to develop in the prototyping phase. The models are the algorithms that get evaluated before they are deployed to production into what we call at BBVA Data & Analytics a “data engineâ€. Whenever the experience supported by the “data engine†does not perform as expected, the problem needs to be reformulated to continue the cyclical process of constant refinement. The scientific method is similar to any design approach that forms and makes new appreciations as new iterations are necessary. Yet, it is not an open-ended process. It has a clear start and end but no definite timeline. Data scientist Neal Lathia argues that “cross-disciplinary work is hard, until you’re speaking the same languageâ€. Additionally, I believe designers and data scientists must immerse themselves in the other’s practice to build a common rhythm. So far, I codified several important touchpoints for designers and data scientists to produce a meaningful user experience powered by algorithms. They must: This intertwined collaboration illustrates a new type of design that I am trying to articulate. In a recent article Harry West CEO at frog suggested the term ‘design of system behavior’: “Human-centered design has expanded from the design of objects (industrial design) to the design of experiences (adding interaction design, visual design, and the design of spaces) and the next step will be the design of system behavior: the design of the algorithms that determine the behavior of automated or intelligent systemsâ€â€Šâ€” Harry West So far I have argued that “living experiences†emerge at the crossroad of data science and design. An indispensable first step is for designers and data scientists is to establish a tangible vision and its outcomes (e.g. experience, solution, priorities, goals, scope and awareness of feasibility). Airbnb Director of Product Jonathan Golden calls that a vision-driven product management approach: “Your company vision is what you want the world to look like in five-plus years — outcomes are the team mandates that will help you get there.†— Jonathan Golden However, that conceptualization phase requires that visions live not just as flat perfect things for board room PowerPoint. Therefore, one of my approaches is to engage the design/science partnership to produce Design Fictions. It has similarities with Amazon’s Working Backward’ process as described by Werner Vogels: “You start with your customer and work your way backwards until you get to the minimum set of technology requirements to satisfy what you try to achieve. The goal is to drive simplicity through a continuous, explicit customer focus.â€â€Šâ€” Werner Vogels Thinking by doing with Design Fiction creates potential futures of a technology to clarify the present. Schema inspired by the Futures Cones and Matt Jones: Jumping to the End — Practical Design Fiction. Design Fiction aims at making tangible the evolution of technologies, the language used to describe them, the rituals, the magic moments, the frustrations, and why not the “offboarding experience”. It helps the different stakeholders of a project to engage with essential questions to understand what the desired experience means and why the team should build it. What are the implications of purchasing that next generation Garden Sensor? What can you do with it? What aren’t you allowed to do? What won’t you do anymore? How does a human interact with that technology the first time, and then routinely after a month, one year or more? Creative and tangible answers to these questions can come to life before a project even starts with the creation of fictional customer reviews, user manual, press release, ads. That material is a way to bring the future to present or as we say at the Near Future Laboratory: “The Design Fictions act as a totem for discussion and evaluation of changes that could bend visions of the desirable and planning of what is necessary.†At BBVA Data & Analytics, this means that I gather data scientists and designers with the objective of creating a tangible vision of their research agenda. First, we first map the ongoing lines of investigations. Then we project their evolution into 2 or 3 iterations wondering: What would the potential resulting technology look like? Where could it be used? Who would use it and for what type of experience? Each participant uses the template of a fictional ad to tell stories with practical answers to these questions. Together we group them into future concepts. We collect all the material and promote the most promising concepts. After that, we share these results internally in series of paper and video advertisements that describe the main features, attributes, characteristics of the experience from our point of view (the feasible) and the user’s point of view (the desirable). This type of fictional material allows both designers and data scientists to feel and get a practical understanding of the technology and its experience. The results help build credibility, enlist support, counter skepticism, create momentum and share a common vision. Finally, the feedback of people with different perspectives allows to anticipate opportunities and challenges. With the advance of machine learning and “artificial intelligence†(AI), it became the responsibility of both designers and data scientists to understand how to shape experiences that improve lives. Or as Greg Borenstein argues in Power to the People: How One Unknown Group of Researchers Holds the Key to Using AI to Solve Real Human Problems: “What’s needed for AI’s wide adoption is an understanding of how to build interfaces that put the power of these systems in the hands of their human users.†— Greg Borenstein That type of design of system behavior represents a future in the tight partnership between design and data science. So far in that journey of creating meaningful experiences in the machine learning era, I can articulate the following characteristics: This is an extended transcript of a talk I gave at the Design Wednesdays event at the BBVA Innovation Center in Madrid on September 21, 2016. Many thanks to the BBVA Design team for their invitation and the quality of the organization!', 'summary' => '<p>This article by author Fabien Girardin discusses the duties in his position as a designer at "B.B.V.A. Data and Analytics Company. He assists in the design of systems to provide enhanced user interactions of various software products by using comprehensive machine learning methods with teams of fellow data scientists with the company. What data scientist and designers in his company like others are doing in the new are of machine learning is upgrading their design systems into systems that uses machine learning by use of artificial intelligence, to have software in items like online shopping, banking and decision recommending predict the user's product preferences, selections, and purchase decisions based on data the machines learn from the users. In this article, Mr. Girardin’s goal is to improve on user experience with design systems his company is responsible for. He believes the new practice of providing the enhanced user satisfaction will beneficial for the customer and the company for 3 reasons. The first is that it will create new types of customer interactions. This means as new technology and software comes out, it will be the"systems designers plus data scientists" jobs to work together to create ways for the machine to learn from the user so that data can be gathered to better understand user preferences for example, movie and music genres. In other words, the more the customer uses the machine more the machine learns. The second reason is the evolution of the relationship between the user and the machine. In this article, Alexa is used as an example, and in its marketing it is used to do everything from turning on the lights to ordering pizza. It has a human and machine helper relationship and this is done by machine learning as the user continues to use the Alexa device. The third reason is in the teamwork relationship between the designers and data scientists." In the field of machine learning, "systems designers plus data scientists will be need to work together to create the system. The designers to create the user friendly interface and the data scientists enable the system to learn from the data it collects. 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=> '', 'rss_cover_image' => '', 'rss_export_link' => 'https://summurai.com/rss/user-experience-fomo', 'hide_embed_iframe_header' => '0', 'hide_embed_iframe_footer' => '0', 'allow_export_text' => '0', 'allow_export_rtf' => '0', 'allow_export_audio' => '0', 'allow_export_image' => '0', 'allow_export_csv' => '0', 'export_alt_head_foot' => '0', 'export_hide_powerby' => '0', 'export_alt_code' => '', 'crm_type' => '0', 'hubspot_access_token' => '', 'hubspot_client_secret' => '', 'show_reg_company_name' => '1', 'show_reg_job_title' => '1', 'show_reg_scheduling' => '0', 'reg_consent_text' => '', 'from_app' => '0', 'from_embed_playlist' => '0', 'is_blocked' => '0', 'is_deleted' => '0', 'active_date' => '2023-09-27 20:47:48', 'created' => '2019-06-22 09:37:01', 'modified' => '2024-04-24 10:12:59' ) ) $lead_id = (int) 0 $title_for_layout = 'Summy | Experience Design in the Machine Learning Era' $permissions = null $logedin_user_details = null $item_title = 'Experience Design in the Machine Learning Era' $item_summary = 'This article by author Fabien Girardin discusses the duties in his position as a designer at "B.B.V.A. Data and Analytics Company. He assists in the design of systems to provide enhanced user interactions of various software products by using comprehensive machine learning methods with teams of fellow data scientists with the company. What data scientist and designers in his company like others are doing in the new are of machine learning is upgrading their design systems into systems that uses machine learning by use of artificial intelligence, to have software in items like online shopping, banking and decision recommending predict the user's product preferences, selections, and purchase decisions based on data the machines learn from the users. In this article, Mr. Girardin’s goal is to improve on user experience with design systems his company is responsible for. He believes the new practice of providing the enhanced user satisfaction will beneficial for the customer and the company for 3 reasons. The first is that it will create new types of customer interactions. This means as new technology and software comes out, it will be the"systems designers plus data scientists" jobs to work together to create ways for the machine to learn from the user so that data can be gathered to better understand user preferences for example, movie and music genres. In other words, the more the customer uses the machine more the machine learns. The second reason is the evolution of the relationship between the user and the machine. In this article, Alexa is used as an example, and in its marketing it is used to do everything from turning on the lights to ordering pizza. It has a human and machine helper relationship and this is done by machine learning as the user continues to use the Alexa device. The third reason is in the teamwork relationship between the designers and data scientists." In the field of machine learning, "systems designers plus data scientists will be need to work together to create the system. The designers to create the user friendly interface and the data scientists enable the system to learn from the data it collects. In conclusion, the subject of machine learning is clearly explained in this article.' $item_site_name = 'BBVA Data & Analytics' $voice_url = 'https://summarytime.com/uploads/voice_file/7190.MP3' $route_show_url = 'https://summurai.com/'
include - APP/View/Article/landing.ctp, line 220 View::_evaluate() - CORE/Cake/View/View.php, line 948 View::_render() - CORE/Cake/View/View.php, line 910 View::render() - CORE/Cake/View/View.php, line 471 Controller::render() - CORE/Cake/Controller/Controller.php, line 954 Dispatcher::_invoke() - CORE/Cake/Routing/Dispatcher.php, line 198 Dispatcher::dispatch() - CORE/Cake/Routing/Dispatcher.php, line 165 [main] - APP/webroot/index.php, line 108
Notice (8): Undefined index: Client [APP/View/Article/landing.ctp, line 221]Thanks a lot. We'll get back to you soon.Code Context<div class="thumbs-icon"><?php echo $this->Html->image(($brand_details['Client']['pseudo_language_id']==2?'images/thumbs-up-icon.jpg':'images/thumbs-up-icon-eng.png'), array('alt'=>'','class'=>''));?></div><h2><?php echo (($brand_details['Client']['pseudo_language_id']==2)?'אנחנו על זה.':"We're on it.");?></h2><p><?php echo (($brand_details['Client']['pseudo_language_id']==2)?'תודה רבה. נתייחס בהקדם.':"Thanks a lot. We'll get back to you soon.");?></p>$viewFile = '/home/summarytime/summurai.com/app/View/Article/landing.ctp' $dataForView = array( 'data' => array( 'MyItem' => array( 'id' => '7190', 'user_master_id' => '188', 'guid' => null, 'posted_by' => '332', 'voice_by' => '1561', 'post_market_id' => '5399', 'image_url' => 'http://www.bbvadata.com/wp-content/uploads/2016/12/discover-weekly-ml.jpg', 'title' => 'Experience Design in the Machine Learning Era', 'other_title' => '', 'description' => 'Traditionally the experience of a digital service follows pre-defined user journeys with clear states and actions. Until recently, it has been the designer’s job to create these linear workflows and transform them into understandable and unobtrusive experiences. This is the story of how that practice is about to change. Over the last 6 months, I have been working in a rather unique position at BBVA Data & Analytics, a center of excellence in financial data analysis. My job is to make the design of user experiences reach a new frontier with the emergence of machine learning techniques. My responsibility — among other things — is to bring a holistic experience design to teams of data scientists and make it an essential part of the lifecycle of algorithmic solutions (e.g. predictive models, recommender systems). In parallel, I perform creative and strategic reviews of experiences that design teams produce (e.g. online banking, online shopping, smart decision making) to steer their evolution into a future of “artificial intelligenceâ€. Practically, I boost the partnerships between teams of designers and data scientists to envision desirable and feasible experiences powered by data and algorithms. Nowadays, the design of many digital services does not only rely on data manipulation and information design but also on systems that learn from their users. If you would open the hood of these systems, you would see that behavioral data (e.g. human interactions, transactions with systems) is fed as context to algorithms that generates knowledge. An interface communicates that knowledge to enrich an experience. Ideally, that experience seeks explicit user actions or implicit sensor events to create a feedback loop that will feed the algorithm with learning material. Discovery Weekly is Spotify’s automated music recommendations “data engine†that brings two hours of custom-made music recommendations, tailored specifically to each Spotify user every Monday. The Discover Weekly’s recommender system leverages the millions playlists that Spotify users create. It gives extra weight to the company’s own experts playlists and those with more followers. The algorithm attempts to augment a person’s listening habits with those with similar tastes. It does it in three main tasks: A typical Discover Weekly playlist recommends 30 songs, a big enough set to discover music that matches with a personal taste among other false positives. That experience provokes the curation of thousands of new playlists that are fed back into the algorithm a week after to generate new recommendations. These feedback loop mechanisms typically offer ways to personalize, optimize or automate existing services. They also create opportunities to design new experiences based on recommendations, predictions or contextualization. At BBVA Data & Analytics I came up with a first non-comprehensive list: We have seen that recommender systems help discover the known unknown or even the unknown unknowns. For instance, Spotify helps discover music through a personalized experience defined on the match between an individual listening behavior and the listening behavior of hundreds of thousands of other individuals. That type of experience has at least three major design challenges. First, recommenders systems have a tendency to create a “filter bubble†that limits suggestions (e.g. products, restaurants, news items, people to connect with) to a world that is strictly linked to a profile built on past behaviors. In response, data scientists must sometimes tweak their algorithms to be less accurate and add a dose of randomness to the suggestions. Second, it is also good design practice to let an open door for users to reshape aspects of their profile that influence the discovery. I would call that feature “profile detoxâ€. Amazon for example allows users to remove items that might negatively influence the recommendations. Imagine the customers purchase gifts for others and those gifts are not necessarily material for future personalized recommendations. Finally, organizations that rely on subjective recommendation like Spotify now enlist humans to give more subjectivity and diversity to the suggested music. This approach of using humans to clean datasets or mitigate the limitations of machine learning algorithm is commonly called “Human Computation†or “Interactive Machine Learningâ€. Data and algorithms also provide means to personalize decision making. For instance at BBVA Data & Analytics we developed advanced techniques to advise BBVA customers on their finance. For example, we consider the temporal evolution of account balances to segment savings behaviors. With that technique we are able to personalize investment opportunities according to each customer’s capacity to save money. This type of algorithms that leads to decision-making needs to learn to be more precise, simply because they often rely on datasets that only give a perspective of reality. In the case of financial advisory, a customer could operate multiple accounts with other banks preventing a clear view on on saving behaviors. It proved a good design practice to let users tell implicitly or explicitly about poor information. It is the data scientist’s responsibility to express the types of feedback that enrich their models and the designer’s job to find ways to make it part of the experience. Traditionally the design of computer programs follows a binary logic with an explicit finite set of concrete and predictable states translated into a workflow. Machine learning algorithms change this with their inherent fuzzy logic. They are designed to look for patterns within a set of sample behaviors to probabilistically approximate the rules of these behaviors (see Machine Learning for Designers for a more detailed introduction to the topic). This approach comes with a certain degree imprecision and unpredictable behaviors. They often return some information on the precision of the information given. For example the booking platform Kayak predicts the evolution of prices according to the analysis of historical prices changes. Its “farecasting†algorithm is designed to return confidence on whether it is a favorable moment to purchase a ticket (see The Machine Learning Behind Farecast). A data scientist is naturally inclined to measure how accurately the algorithm predicts a value: “We predict this fare will be xâ€. That ‘prediction’ is in fact an information based on historical trends. Yet predicting is not the same as informing and a designer must consider how well such a prediction could support a user action: “Buy! this fare is likely to increaseâ€. The ‘likely’ with an overview of the price trend is an example of a “beautiful seam†in the user experience, a notion coined by Mark Weiser at the time of the Xerox Palo Alto Research Center and further developed by Chalmers and MacColl as seamful design: Seamful design is about exploiting failures and limitations to improve the experience. It is about improving the system allowing users to tell about poor recommendations. DJ Patil describes subtle techniques in Data Jujitsu. The ideal for an algorithm is to deliver high precision and recall scores. Unfortunately, precision and recall often work against each other. There is often a need to take design decisions with the trade-off between precision versus recall. For instance, in Spotify Discovery Weekly, a design decision had to be taken to define the size of playlists according to the performance of the recommender system. A large playlist highlights the confidence of Spotify to deliver a rather large inventory of 30 songs, a wide-enough set to increase the opportunities for users to stumble on perfect recommendations. Today, what we read online is based on our own behaviors and the behaviors of other users. Algorithms typically score the relevance of social and news content. The aim of these algorithms is to promote content for higher engagement or send notifications to create habits. Obviously these actions taken on our behalf are not necessarily for our own interest. In the attention economy, both designers and data scientists should learn from the anxieties, obsessions, phobias, stress and other mental burdens of the connected humans. Source: The Global Village and its Discomforts. Photo courtesy of Nicolas Nova. Arguably, we entered into the attention economy, and major online services are fighting to hook people, grap their attention for as long as possible. Their business is to keep users active as long and frequently as possible on their platforms. This leads to the development of sticky, needy experiences that often play with emotions like Fear of Missing Out (FoMO) or other obsessions to dope the user engagement. The actors of the attention economy use also techniques that promote addiction such as Variable Schedule Rewards. It is the exact same mechanisms as the ones used in slot machines. The resulting experience promotes the service’s interest (the casino) hooking people endlessly searching for the next reward. Our mobile phones have become those slot machines of notifications, alerts, messages, retweets, likes, that some of us check on an average 150 times per day if not more. Today designer can use data and algorithms to exploit cognitive vulnerabilities of people in their everyday lives. That new power raises the need for new design principles in the age of machine learning (see The ethics of good design: A principle for the connected age). There are opportunities to design a radically different experience than engagement. Indeed, an organization like a bank has the advantage of being a business that runs on data and does not need customers to spend the maximum amount of time with their services. Tristan Harris’ Time Well Spent movement is particularly inspiring in that sense. He promotes the type of experience that use data to be super-relevant or be silent. The type of technology to protect the user focus and to be respectful of people’s time. The Twitter “While you were away…†is a compelling example of that practice. Other services are good at suggesting moments to engage with them. Instead of measuring user retention, that type of experience focuses on how relevant the interactions are. Data scientist are good in detecting normal behavior and abnormal situations. At BBVA Data & Analytics we are working to promote a peace of mind to BBVA customers with mechanisms that gives a general awareness when things are fine and that trigger more detailed information on abnormal situations. More generally, we believe current generation of machine learning brings new powers to society, but also increases the responsibility of their creators. Algorithmic bias exists and may be inherent to the data sources. In consequence, there is a particular need to make algorithms more legible for people and auditable by regulators to understand their implications. Practically, this means knowledge that the an algorithm produces should safeguard the interest of their users and the results of the evaluation and the criteria used should be explained. In the previous section we have seen that the experiences powered by machine learning are not linear or based on static business and design rules. They evolves according to human behaviors with constantly updating models fed by streams of data. Each product or service becomes almost like a living, breathing thing. Or as people at Google would say: “It’s a different kind of engineeringâ€. I would argue that it is also a different kind of design. For instance, Amazon explains Echo’s braininess as a thing that “continually learns and adds more functionality over timeâ€. This description highlights the need to design the experience for systems to learn from human behavior. Consequently, beyond considering the first contact and the onboarding experience, that type of product or service requires considerations on their use after 1 hour, 1 day, 1 year, etc. If you look at the promotional video of the Edyn garden sensor you will notice the evolution of the experience from creating new habits for taking care of a garden to communicating the unknown unknowns about plants, to convey peace of mind on the key metrics, and to guarantee time well spent with some level of watering automation. That type of data product requires a responsible design that considers moments when things start to disappoint, embarrass, annoy or stop working or being useful. The design of the “offboarding experience†could become almost as important as the “onboarding experienceâ€. For instance, allegedly a third of the Fitbit users stop wearing the device within 6 months. What happens to these millions of abandoned connected objects? What happens to the data and intelligence on the individual they produced? What are the opportunities to use them in different experiences? Products characterized by an experience that evolves according to behavioral data that constantly feed algorithms (e.g. Fitbit) are living products that inevitably also have a tendency to die. Source: The Life and Death of Data Products. There are new ways to imagine the relation after a digital break-up with a product. Digital services work on an increasingly vast ecosystem of things and channels but user data have a tendency to be more centralized. Think about the notion of portable reputation that allows people to use a service based on the relation measured with another service. Looking a bit further into the near future, the recent breakthrough in Natural Language Processing, Knowledge Representation, Voice Recognition and Nature Language Production could create more subtle and stronger relations with machines. In a few iterations, Amazon Echo might start to be much more nurturing. A potential evolution that anthropologist Genevieve Bell foresees a shift from human-computer interactions to human-computer relationships in The next wave of AI is rooted in human culture and history: “So the frame there is not about recommendations, which is where much of AI is now, but is actually about nurture and care. If those become the buzzwords, then you sit in this very interesting moment of being able to pivot from talking about human-computer interactions to human-computer relationships.â€â€Šâ€” Genevieve Bell In this section we have seen that algorithms are getting closer to our everyday lives and that data provide a context for an evolving relationship. The implications of that evolution require most intense collaboration between design and data science. My experience so far envisioning experiences with data and algorithms shows that it is a different practice from current human-centered design. At BBVA Data & Analytics, the role of data scientists has been elevated from reactive model and A/B test developers to proactive partners who think about the implications of their work. Our singular data science teams breaks into sub-teams that partner more directly with engineers, designers, and product managers. At the moment of shaping an experience, we exploit thick data, the qualitative information that provides insights on people’s lives (see Why Big Data Needs Thick Data), big data from the aggregated behavioral data of millions of people and the small data that each individual generates. Classically, designers focus on defining the experience of the service, feature or product. They nest the concept within the larger ecosystem that relates to it. Data scientists develop the algorithms that will support that experience and measure it with A/B testing. The first few weeks in my role at BBVA Data & Analytics, I found designers and data scientists often stuck in deadlocked exchanges that typically sounded like this: The main issue was the lack of shared understanding of each other’s practice and objectives. For instance, designers transform a context into a form of experience. Data scientists transform a context with data and models into knowledge. Designers often adopt a path that adapts to a changing context and new appreciations. Data scientists employ processes similar to humber-center design but are more mechanical and less organic. They strictly follow the scientific methods with its cyclical processes of constant refinement. A properly formulated research question helps define the hypothesis and the types of models to develop in the prototyping phase. The models are the algorithms that get evaluated before they are deployed to production into what we call at BBVA Data & Analytics a “data engineâ€. Whenever the experience supported by the “data engine†does not perform as expected, the problem needs to be reformulated to continue the cyclical process of constant refinement. The scientific method is similar to any design approach that forms and makes new appreciations as new iterations are necessary. Yet, it is not an open-ended process. It has a clear start and end but no definite timeline. Data scientist Neal Lathia argues that “cross-disciplinary work is hard, until you’re speaking the same languageâ€. Additionally, I believe designers and data scientists must immerse themselves in the other’s practice to build a common rhythm. So far, I codified several important touchpoints for designers and data scientists to produce a meaningful user experience powered by algorithms. They must: This intertwined collaboration illustrates a new type of design that I am trying to articulate. In a recent article Harry West CEO at frog suggested the term ‘design of system behavior’: “Human-centered design has expanded from the design of objects (industrial design) to the design of experiences (adding interaction design, visual design, and the design of spaces) and the next step will be the design of system behavior: the design of the algorithms that determine the behavior of automated or intelligent systemsâ€â€Šâ€” Harry West So far I have argued that “living experiences†emerge at the crossroad of data science and design. An indispensable first step is for designers and data scientists is to establish a tangible vision and its outcomes (e.g. experience, solution, priorities, goals, scope and awareness of feasibility). Airbnb Director of Product Jonathan Golden calls that a vision-driven product management approach: “Your company vision is what you want the world to look like in five-plus years — outcomes are the team mandates that will help you get there.†— Jonathan Golden However, that conceptualization phase requires that visions live not just as flat perfect things for board room PowerPoint. Therefore, one of my approaches is to engage the design/science partnership to produce Design Fictions. It has similarities with Amazon’s Working Backward’ process as described by Werner Vogels: “You start with your customer and work your way backwards until you get to the minimum set of technology requirements to satisfy what you try to achieve. The goal is to drive simplicity through a continuous, explicit customer focus.â€â€Šâ€” Werner Vogels Thinking by doing with Design Fiction creates potential futures of a technology to clarify the present. Schema inspired by the Futures Cones and Matt Jones: Jumping to the End — Practical Design Fiction. Design Fiction aims at making tangible the evolution of technologies, the language used to describe them, the rituals, the magic moments, the frustrations, and why not the “offboarding experience”. It helps the different stakeholders of a project to engage with essential questions to understand what the desired experience means and why the team should build it. What are the implications of purchasing that next generation Garden Sensor? What can you do with it? What aren’t you allowed to do? What won’t you do anymore? How does a human interact with that technology the first time, and then routinely after a month, one year or more? Creative and tangible answers to these questions can come to life before a project even starts with the creation of fictional customer reviews, user manual, press release, ads. That material is a way to bring the future to present or as we say at the Near Future Laboratory: “The Design Fictions act as a totem for discussion and evaluation of changes that could bend visions of the desirable and planning of what is necessary.†At BBVA Data & Analytics, this means that I gather data scientists and designers with the objective of creating a tangible vision of their research agenda. First, we first map the ongoing lines of investigations. Then we project their evolution into 2 or 3 iterations wondering: What would the potential resulting technology look like? Where could it be used? Who would use it and for what type of experience? Each participant uses the template of a fictional ad to tell stories with practical answers to these questions. Together we group them into future concepts. We collect all the material and promote the most promising concepts. After that, we share these results internally in series of paper and video advertisements that describe the main features, attributes, characteristics of the experience from our point of view (the feasible) and the user’s point of view (the desirable). This type of fictional material allows both designers and data scientists to feel and get a practical understanding of the technology and its experience. The results help build credibility, enlist support, counter skepticism, create momentum and share a common vision. Finally, the feedback of people with different perspectives allows to anticipate opportunities and challenges. With the advance of machine learning and “artificial intelligence†(AI), it became the responsibility of both designers and data scientists to understand how to shape experiences that improve lives. Or as Greg Borenstein argues in Power to the People: How One Unknown Group of Researchers Holds the Key to Using AI to Solve Real Human Problems: “What’s needed for AI’s wide adoption is an understanding of how to build interfaces that put the power of these systems in the hands of their human users.†— Greg Borenstein That type of design of system behavior represents a future in the tight partnership between design and data science. So far in that journey of creating meaningful experiences in the machine learning era, I can articulate the following characteristics: This is an extended transcript of a talk I gave at the Design Wednesdays event at the BBVA Innovation Center in Madrid on September 21, 2016. Many thanks to the BBVA Design team for their invitation and the quality of the organization!', 'summary' => '<p>This article by author Fabien Girardin discusses the duties in his position as a designer at "B.B.V.A. Data and Analytics Company. He assists in the design of systems to provide enhanced user interactions of various software products by using comprehensive machine learning methods with teams of fellow data scientists with the company. What data scientist and designers in his company like others are doing in the new are of machine learning is upgrading their design systems into systems that uses machine learning by use of artificial intelligence, to have software in items like online shopping, banking and decision recommending predict the user's product preferences, selections, and purchase decisions based on data the machines learn from the users. In this article, Mr. Girardin’s goal is to improve on user experience with design systems his company is responsible for. He believes the new practice of providing the enhanced user satisfaction will beneficial for the customer and the company for 3 reasons. The first is that it will create new types of customer interactions. This means as new technology and software comes out, it will be the"systems designers plus data scientists" jobs to work together to create ways for the machine to learn from the user so that data can be gathered to better understand user preferences for example, movie and music genres. In other words, the more the customer uses the machine more the machine learns. The second reason is the evolution of the relationship between the user and the machine. In this article, Alexa is used as an example, and in its marketing it is used to do everything from turning on the lights to ordering pizza. It has a human and machine helper relationship and this is done by machine learning as the user continues to use the Alexa device. The third reason is in the teamwork relationship between the designers and data scientists." In the field of machine learning, "systems designers plus data scientists will be need to work together to create the system. The designers to create the user friendly interface and the data scientists enable the system to learn from the data it collects. 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=> '7190', 'user_master_id' => '188', 'guid' => null, 'posted_by' => '332', 'voice_by' => '1561', 'post_market_id' => '5399', 'image_url' => 'http://www.bbvadata.com/wp-content/uploads/2016/12/discover-weekly-ml.jpg', 'title' => 'Experience Design in the Machine Learning Era', 'other_title' => '', 'description' => 'Traditionally the experience of a digital service follows pre-defined user journeys with clear states and actions. Until recently, it has been the designer’s job to create these linear workflows and transform them into understandable and unobtrusive experiences. This is the story of how that practice is about to change. Over the last 6 months, I have been working in a rather unique position at BBVA Data & Analytics, a center of excellence in financial data analysis. My job is to make the design of user experiences reach a new frontier with the emergence of machine learning techniques. My responsibility — among other things — is to bring a holistic experience design to teams of data scientists and make it an essential part of the lifecycle of algorithmic solutions (e.g. predictive models, recommender systems). In parallel, I perform creative and strategic reviews of experiences that design teams produce (e.g. online banking, online shopping, smart decision making) to steer their evolution into a future of “artificial intelligenceâ€. Practically, I boost the partnerships between teams of designers and data scientists to envision desirable and feasible experiences powered by data and algorithms. Nowadays, the design of many digital services does not only rely on data manipulation and information design but also on systems that learn from their users. If you would open the hood of these systems, you would see that behavioral data (e.g. human interactions, transactions with systems) is fed as context to algorithms that generates knowledge. An interface communicates that knowledge to enrich an experience. Ideally, that experience seeks explicit user actions or implicit sensor events to create a feedback loop that will feed the algorithm with learning material. Discovery Weekly is Spotify’s automated music recommendations “data engine†that brings two hours of custom-made music recommendations, tailored specifically to each Spotify user every Monday. The Discover Weekly’s recommender system leverages the millions playlists that Spotify users create. It gives extra weight to the company’s own experts playlists and those with more followers. The algorithm attempts to augment a person’s listening habits with those with similar tastes. It does it in three main tasks: A typical Discover Weekly playlist recommends 30 songs, a big enough set to discover music that matches with a personal taste among other false positives. That experience provokes the curation of thousands of new playlists that are fed back into the algorithm a week after to generate new recommendations. These feedback loop mechanisms typically offer ways to personalize, optimize or automate existing services. They also create opportunities to design new experiences based on recommendations, predictions or contextualization. At BBVA Data & Analytics I came up with a first non-comprehensive list: We have seen that recommender systems help discover the known unknown or even the unknown unknowns. For instance, Spotify helps discover music through a personalized experience defined on the match between an individual listening behavior and the listening behavior of hundreds of thousands of other individuals. That type of experience has at least three major design challenges. First, recommenders systems have a tendency to create a “filter bubble†that limits suggestions (e.g. products, restaurants, news items, people to connect with) to a world that is strictly linked to a profile built on past behaviors. In response, data scientists must sometimes tweak their algorithms to be less accurate and add a dose of randomness to the suggestions. Second, it is also good design practice to let an open door for users to reshape aspects of their profile that influence the discovery. I would call that feature “profile detoxâ€. Amazon for example allows users to remove items that might negatively influence the recommendations. Imagine the customers purchase gifts for others and those gifts are not necessarily material for future personalized recommendations. Finally, organizations that rely on subjective recommendation like Spotify now enlist humans to give more subjectivity and diversity to the suggested music. This approach of using humans to clean datasets or mitigate the limitations of machine learning algorithm is commonly called “Human Computation†or “Interactive Machine Learningâ€. Data and algorithms also provide means to personalize decision making. For instance at BBVA Data & Analytics we developed advanced techniques to advise BBVA customers on their finance. For example, we consider the temporal evolution of account balances to segment savings behaviors. With that technique we are able to personalize investment opportunities according to each customer’s capacity to save money. This type of algorithms that leads to decision-making needs to learn to be more precise, simply because they often rely on datasets that only give a perspective of reality. In the case of financial advisory, a customer could operate multiple accounts with other banks preventing a clear view on on saving behaviors. It proved a good design practice to let users tell implicitly or explicitly about poor information. It is the data scientist’s responsibility to express the types of feedback that enrich their models and the designer’s job to find ways to make it part of the experience. Traditionally the design of computer programs follows a binary logic with an explicit finite set of concrete and predictable states translated into a workflow. Machine learning algorithms change this with their inherent fuzzy logic. They are designed to look for patterns within a set of sample behaviors to probabilistically approximate the rules of these behaviors (see Machine Learning for Designers for a more detailed introduction to the topic). This approach comes with a certain degree imprecision and unpredictable behaviors. They often return some information on the precision of the information given. For example the booking platform Kayak predicts the evolution of prices according to the analysis of historical prices changes. Its “farecasting†algorithm is designed to return confidence on whether it is a favorable moment to purchase a ticket (see The Machine Learning Behind Farecast). A data scientist is naturally inclined to measure how accurately the algorithm predicts a value: “We predict this fare will be xâ€. That ‘prediction’ is in fact an information based on historical trends. Yet predicting is not the same as informing and a designer must consider how well such a prediction could support a user action: “Buy! this fare is likely to increaseâ€. The ‘likely’ with an overview of the price trend is an example of a “beautiful seam†in the user experience, a notion coined by Mark Weiser at the time of the Xerox Palo Alto Research Center and further developed by Chalmers and MacColl as seamful design: Seamful design is about exploiting failures and limitations to improve the experience. It is about improving the system allowing users to tell about poor recommendations. DJ Patil describes subtle techniques in Data Jujitsu. The ideal for an algorithm is to deliver high precision and recall scores. Unfortunately, precision and recall often work against each other. There is often a need to take design decisions with the trade-off between precision versus recall. For instance, in Spotify Discovery Weekly, a design decision had to be taken to define the size of playlists according to the performance of the recommender system. A large playlist highlights the confidence of Spotify to deliver a rather large inventory of 30 songs, a wide-enough set to increase the opportunities for users to stumble on perfect recommendations. Today, what we read online is based on our own behaviors and the behaviors of other users. Algorithms typically score the relevance of social and news content. The aim of these algorithms is to promote content for higher engagement or send notifications to create habits. Obviously these actions taken on our behalf are not necessarily for our own interest. In the attention economy, both designers and data scientists should learn from the anxieties, obsessions, phobias, stress and other mental burdens of the connected humans. Source: The Global Village and its Discomforts. Photo courtesy of Nicolas Nova. Arguably, we entered into the attention economy, and major online services are fighting to hook people, grap their attention for as long as possible. Their business is to keep users active as long and frequently as possible on their platforms. This leads to the development of sticky, needy experiences that often play with emotions like Fear of Missing Out (FoMO) or other obsessions to dope the user engagement. The actors of the attention economy use also techniques that promote addiction such as Variable Schedule Rewards. It is the exact same mechanisms as the ones used in slot machines. The resulting experience promotes the service’s interest (the casino) hooking people endlessly searching for the next reward. Our mobile phones have become those slot machines of notifications, alerts, messages, retweets, likes, that some of us check on an average 150 times per day if not more. Today designer can use data and algorithms to exploit cognitive vulnerabilities of people in their everyday lives. That new power raises the need for new design principles in the age of machine learning (see The ethics of good design: A principle for the connected age). There are opportunities to design a radically different experience than engagement. Indeed, an organization like a bank has the advantage of being a business that runs on data and does not need customers to spend the maximum amount of time with their services. Tristan Harris’ Time Well Spent movement is particularly inspiring in that sense. He promotes the type of experience that use data to be super-relevant or be silent. The type of technology to protect the user focus and to be respectful of people’s time. The Twitter “While you were away…†is a compelling example of that practice. Other services are good at suggesting moments to engage with them. Instead of measuring user retention, that type of experience focuses on how relevant the interactions are. Data scientist are good in detecting normal behavior and abnormal situations. At BBVA Data & Analytics we are working to promote a peace of mind to BBVA customers with mechanisms that gives a general awareness when things are fine and that trigger more detailed information on abnormal situations. More generally, we believe current generation of machine learning brings new powers to society, but also increases the responsibility of their creators. Algorithmic bias exists and may be inherent to the data sources. In consequence, there is a particular need to make algorithms more legible for people and auditable by regulators to understand their implications. Practically, this means knowledge that the an algorithm produces should safeguard the interest of their users and the results of the evaluation and the criteria used should be explained. In the previous section we have seen that the experiences powered by machine learning are not linear or based on static business and design rules. They evolves according to human behaviors with constantly updating models fed by streams of data. Each product or service becomes almost like a living, breathing thing. Or as people at Google would say: “It’s a different kind of engineeringâ€. I would argue that it is also a different kind of design. For instance, Amazon explains Echo’s braininess as a thing that “continually learns and adds more functionality over timeâ€. This description highlights the need to design the experience for systems to learn from human behavior. Consequently, beyond considering the first contact and the onboarding experience, that type of product or service requires considerations on their use after 1 hour, 1 day, 1 year, etc. If you look at the promotional video of the Edyn garden sensor you will notice the evolution of the experience from creating new habits for taking care of a garden to communicating the unknown unknowns about plants, to convey peace of mind on the key metrics, and to guarantee time well spent with some level of watering automation. That type of data product requires a responsible design that considers moments when things start to disappoint, embarrass, annoy or stop working or being useful. The design of the “offboarding experience†could become almost as important as the “onboarding experienceâ€. For instance, allegedly a third of the Fitbit users stop wearing the device within 6 months. What happens to these millions of abandoned connected objects? What happens to the data and intelligence on the individual they produced? What are the opportunities to use them in different experiences? Products characterized by an experience that evolves according to behavioral data that constantly feed algorithms (e.g. Fitbit) are living products that inevitably also have a tendency to die. Source: The Life and Death of Data Products. There are new ways to imagine the relation after a digital break-up with a product. Digital services work on an increasingly vast ecosystem of things and channels but user data have a tendency to be more centralized. Think about the notion of portable reputation that allows people to use a service based on the relation measured with another service. Looking a bit further into the near future, the recent breakthrough in Natural Language Processing, Knowledge Representation, Voice Recognition and Nature Language Production could create more subtle and stronger relations with machines. In a few iterations, Amazon Echo might start to be much more nurturing. A potential evolution that anthropologist Genevieve Bell foresees a shift from human-computer interactions to human-computer relationships in The next wave of AI is rooted in human culture and history: “So the frame there is not about recommendations, which is where much of AI is now, but is actually about nurture and care. If those become the buzzwords, then you sit in this very interesting moment of being able to pivot from talking about human-computer interactions to human-computer relationships.â€â€Šâ€” Genevieve Bell In this section we have seen that algorithms are getting closer to our everyday lives and that data provide a context for an evolving relationship. The implications of that evolution require most intense collaboration between design and data science. My experience so far envisioning experiences with data and algorithms shows that it is a different practice from current human-centered design. At BBVA Data & Analytics, the role of data scientists has been elevated from reactive model and A/B test developers to proactive partners who think about the implications of their work. Our singular data science teams breaks into sub-teams that partner more directly with engineers, designers, and product managers. At the moment of shaping an experience, we exploit thick data, the qualitative information that provides insights on people’s lives (see Why Big Data Needs Thick Data), big data from the aggregated behavioral data of millions of people and the small data that each individual generates. Classically, designers focus on defining the experience of the service, feature or product. They nest the concept within the larger ecosystem that relates to it. Data scientists develop the algorithms that will support that experience and measure it with A/B testing. The first few weeks in my role at BBVA Data & Analytics, I found designers and data scientists often stuck in deadlocked exchanges that typically sounded like this: The main issue was the lack of shared understanding of each other’s practice and objectives. For instance, designers transform a context into a form of experience. Data scientists transform a context with data and models into knowledge. Designers often adopt a path that adapts to a changing context and new appreciations. Data scientists employ processes similar to humber-center design but are more mechanical and less organic. They strictly follow the scientific methods with its cyclical processes of constant refinement. A properly formulated research question helps define the hypothesis and the types of models to develop in the prototyping phase. The models are the algorithms that get evaluated before they are deployed to production into what we call at BBVA Data & Analytics a “data engineâ€. Whenever the experience supported by the “data engine†does not perform as expected, the problem needs to be reformulated to continue the cyclical process of constant refinement. The scientific method is similar to any design approach that forms and makes new appreciations as new iterations are necessary. Yet, it is not an open-ended process. It has a clear start and end but no definite timeline. Data scientist Neal Lathia argues that “cross-disciplinary work is hard, until you’re speaking the same languageâ€. Additionally, I believe designers and data scientists must immerse themselves in the other’s practice to build a common rhythm. So far, I codified several important touchpoints for designers and data scientists to produce a meaningful user experience powered by algorithms. They must: This intertwined collaboration illustrates a new type of design that I am trying to articulate. In a recent article Harry West CEO at frog suggested the term ‘design of system behavior’: “Human-centered design has expanded from the design of objects (industrial design) to the design of experiences (adding interaction design, visual design, and the design of spaces) and the next step will be the design of system behavior: the design of the algorithms that determine the behavior of automated or intelligent systemsâ€â€Šâ€” Harry West So far I have argued that “living experiences†emerge at the crossroad of data science and design. An indispensable first step is for designers and data scientists is to establish a tangible vision and its outcomes (e.g. experience, solution, priorities, goals, scope and awareness of feasibility). Airbnb Director of Product Jonathan Golden calls that a vision-driven product management approach: “Your company vision is what you want the world to look like in five-plus years — outcomes are the team mandates that will help you get there.†— Jonathan Golden However, that conceptualization phase requires that visions live not just as flat perfect things for board room PowerPoint. Therefore, one of my approaches is to engage the design/science partnership to produce Design Fictions. It has similarities with Amazon’s Working Backward’ process as described by Werner Vogels: “You start with your customer and work your way backwards until you get to the minimum set of technology requirements to satisfy what you try to achieve. The goal is to drive simplicity through a continuous, explicit customer focus.â€â€Šâ€” Werner Vogels Thinking by doing with Design Fiction creates potential futures of a technology to clarify the present. Schema inspired by the Futures Cones and Matt Jones: Jumping to the End — Practical Design Fiction. Design Fiction aims at making tangible the evolution of technologies, the language used to describe them, the rituals, the magic moments, the frustrations, and why not the “offboarding experience”. It helps the different stakeholders of a project to engage with essential questions to understand what the desired experience means and why the team should build it. What are the implications of purchasing that next generation Garden Sensor? What can you do with it? What aren’t you allowed to do? What won’t you do anymore? How does a human interact with that technology the first time, and then routinely after a month, one year or more? Creative and tangible answers to these questions can come to life before a project even starts with the creation of fictional customer reviews, user manual, press release, ads. That material is a way to bring the future to present or as we say at the Near Future Laboratory: “The Design Fictions act as a totem for discussion and evaluation of changes that could bend visions of the desirable and planning of what is necessary.†At BBVA Data & Analytics, this means that I gather data scientists and designers with the objective of creating a tangible vision of their research agenda. First, we first map the ongoing lines of investigations. Then we project their evolution into 2 or 3 iterations wondering: What would the potential resulting technology look like? Where could it be used? Who would use it and for what type of experience? Each participant uses the template of a fictional ad to tell stories with practical answers to these questions. Together we group them into future concepts. We collect all the material and promote the most promising concepts. After that, we share these results internally in series of paper and video advertisements that describe the main features, attributes, characteristics of the experience from our point of view (the feasible) and the user’s point of view (the desirable). This type of fictional material allows both designers and data scientists to feel and get a practical understanding of the technology and its experience. The results help build credibility, enlist support, counter skepticism, create momentum and share a common vision. Finally, the feedback of people with different perspectives allows to anticipate opportunities and challenges. With the advance of machine learning and “artificial intelligence†(AI), it became the responsibility of both designers and data scientists to understand how to shape experiences that improve lives. Or as Greg Borenstein argues in Power to the People: How One Unknown Group of Researchers Holds the Key to Using AI to Solve Real Human Problems: “What’s needed for AI’s wide adoption is an understanding of how to build interfaces that put the power of these systems in the hands of their human users.†— Greg Borenstein That type of design of system behavior represents a future in the tight partnership between design and data science. So far in that journey of creating meaningful experiences in the machine learning era, I can articulate the following characteristics: This is an extended transcript of a talk I gave at the Design Wednesdays event at the BBVA Innovation Center in Madrid on September 21, 2016. Many thanks to the BBVA Design team for their invitation and the quality of the organization!', 'summary' => '<p>This article by author Fabien Girardin discusses the duties in his position as a designer at "B.B.V.A. Data and Analytics Company. He assists in the design of systems to provide enhanced user interactions of various software products by using comprehensive machine learning methods with teams of fellow data scientists with the company. What data scientist and designers in his company like others are doing in the new are of machine learning is upgrading their design systems into systems that uses machine learning by use of artificial intelligence, to have software in items like online shopping, banking and decision recommending predict the user's product preferences, selections, and purchase decisions based on data the machines learn from the users. In this article, Mr. Girardin’s goal is to improve on user experience with design systems his company is responsible for. He believes the new practice of providing the enhanced user satisfaction will beneficial for the customer and the company for 3 reasons. The first is that it will create new types of customer interactions. This means as new technology and software comes out, it will be the"systems designers plus data scientists" jobs to work together to create ways for the machine to learn from the user so that data can be gathered to better understand user preferences for example, movie and music genres. In other words, the more the customer uses the machine more the machine learns. The second reason is the evolution of the relationship between the user and the machine. In this article, Alexa is used as an example, and in its marketing it is used to do everything from turning on the lights to ordering pizza. It has a human and machine helper relationship and this is done by machine learning as the user continues to use the Alexa device. The third reason is in the teamwork relationship between the designers and data scientists." In the field of machine learning, "systems designers plus data scientists will be need to work together to create the system. The designers to create the user friendly interface and the data scientists enable the system to learn from the data it collects. 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'is_sms_active' => '1', 'is_email_active' => '1', 'available_credits' => '1', 'activation' => '', 'type' => '2', 'auto_approve' => '1', 'ip' => '', 'pass_code' => '1482468698585cad5ab8c57', 'company_id' => '0', 'stripe_customer_id' => '', 'is_company_user' => '0', 'change_password_count' => '0', 'is_vip' => false, 'time_zone' => 'GMT-5', 'show_on_sign_in' => '1', 'is_system_user' => '0', 'notifications_for_subcription' => '1', 'payment_platform_id' => '1', 'payment_model' => '0', 'monthly_rate' => '0', 'price_factor' => '1', 'voice_hero_price' => '0', 'monthly_free_credits' => '0', 'is_endless' => '0', 'rank_master_id' => '2', 'from_conference' => false, 'is_blogger' => false, 'sub_title' => '', 'cover_image' => '', 'curator_structure' => '', 'is_curator' => '0', 'form_curator' => false, 'curator_type' => false, 'curator_categories' => '', 'initial_cnt_followers' => '0', 'is_science' => false, 'from_client' => '0', 'is_auto_close_tab' => '1', 'property_category_id' => '0', 'blogger_id' 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=> '', 'select_client' => '0', 'default_user' => '0', 'selected_brand_id' => '0', 'show_explainer' => '0', 'ordering' => '0', 'from_app' => '0', 'from_onboarding' => '0', 'onboarding_status' => '0', 'from_embed_playlist' => '0', 'status' => false, 'last_login' => null, 'created_by' => null, 'modified_by' => '0', 'is_blocked' => false, 'is_deleted' => false, 'created' => '2017-03-08 05:41:52', 'modified' => '2022-03-22 16:09:53' ), 'VoiceBy' => array( 'password' => '*****', 'id' => '1561', 'full_name' => 'Ikwo Ibiam', 'first_name' => '', 'last_name' => '', 'username' => 'ikwo-ibiam', 'email' => '[email protected]', 'gender' => '3', 'description' => '', 'avatar_id' => '6', 'profile_picture' => '', 'profile_image' => null, 'address' => null, 'phone' => null, 'city_id' => '', 'country_id' => 'United States', 'state' => null, 'postalcode' => null, 'latitude' => '', 'longitude' => '', 'phone_skip_count' => '0', 'isd_code' => '', 'website' => '', 'is_sms_active' => '1', 'is_email_active' => 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We'll make sure you don't miss a UX thing.', 'board_section_title' => '<X> items are waiting for you', 'show_board_section_item_count' => '1', 'show_subscription_form' => '0', 'show_playter_box' => '0', 'show_curated_by' => '0', 'show_footer_cta' => '1', 'footer_icon' => '0', 'footer_title' => '', 'footer_sub_title' => '', 'call_to_action_title1' => '', 'call_to_action_url1' => '', 'show_call_to_action2' => '0', 'call_to_action_title2' => '', 'call_to_action_url2' => '', 'player_type' => '0', 'allow_mini_max' => '0', 'cover_style' => '0', 'default_view_style' => '2', 'show_featured_element' => '1', 'show_about_brand_box' => '1', 'show_brand_box_type' => '0', 'brand_title' => 'Brought to you by', 'brand_secondary_text' => 'The Summurai platform and services are all about engaging your audience with audio summary feeds and branded audio playlists, allowing your audience to know more with less effort and offering your brand the chance to stand out.', 'show_brand_box_company' => '1', 'brand_image' => '', 'brand_image_layout' => '2', 'brand_link_name' => 'Visit homepage', 'brand_link_url' => 'http://www.summurai.com', 'show_feedback_box' => '1', 'show_disquss_element' => '0', 'show_full_page_item' => '1', 'show_brand_name' => '1', 'show_brand_link' => '1', 'show_brand_link_type' => '1', 'show_logo_element' => '1', 'show_logo_type' => '1', 'is_send_mobile' => '1', 'send_to_mobile' => '0', 'show_alternate_footer' => '0', 'footer_color' => '#2D383F', 'footer_text_color' => '0', 'alternate_footer_type' => '0', 'alternate_footer_logo' => '', 'show_user_element' => '0', 'show_election_panel' => '0', 'visit_count' => '0', 'mobile_visit_count' => '662', 'unique_count' => '0', 'mobile_unique_count' => '381', 'registration_require' => '0', 'registration_trigger' => '2', 'pre_registration_summy' => '1', 'registration_type' => '0', 'board_template_type' => '0', 'is_allow_playlist' => '0', 'allow_embed_playlist' => '0', 'show_disqus_comments' => '0', 'show_cookies_message' => '0', 'show_web_notification' => '0', 'is_exit_popup' => '0', 'is_allow_map' => '0', 'show_categories' => '0', 'category_title' => '', 'show_category_on_mobile' => '0', 'show_presenter_profile_box' => '0', 'presenter_sec_title' => 'Presented by', 'presenter_name' => '', 'presenter_title' => '', 'presenter_image' => '', 'presenter_image_layout' => '0', 'presenter_btn_text' => '', 'presenter_btn_url' => '', 'show_presenter_btn' => '0', 'show_qrcode' => '1', 'qrcode_title' => 'Listen on the go', 'qrcode_secondary_text' => 'Scan the code with your smartphone to listen later', 'is_allow_changing_view' => '1', 'show_summyboard_search' => '1', 'show_read_indication' => '1', 'show_tags' => '0', 'show_faces' => '0', 'show_multi_lang' => '0', 'multi_lang_default' => '0', 'is_summy_motivation' => '0', 'qrcode_pos' => '1', 'categories_pos' => '2', 'brand_box_pos' => '3', 'feedback_box_pos' => '4', 'presenter_box_pos' => '5', 'credits_box_pos' => '6', 'is_allow_sharing' => '1', 'is_allow_embed' => '1', 'show_sorting_filter' => '0', 'board_social_image' => '', 'post_social_title' => '', 'post_social_sub_title' => '', 'show_register_button' => '0', 'manage_rss' => '0', 'host_sub_domain' => '0', 'host_sub_domain_url' => '', 'main_call_to_action_type' => '0', 'is_extension' => '1', 'welcome_email_template_name' => '', 'welcome_email_template_subject' => '', 'welcome_email_template_message' => '', 'welcome_email_template_item_numbers' => '', 'welcome_text_message' => '', 'update_email_template_name' => '', 'update_email_template_subject' => 'Your Weekly update from UXFOMO', 'update_email_template_message' => 'Another week past and it's time for the next batch of UX updates, straight to your ears.', 'update_email_template_item_numbers' => '350, 351, 352', 'update_text_message' => '', 'send_welcome_email' => '0', 'show_summurai_credit_in_footer' => '1', 'seo_title' => 'Summurai | DON'T MISS A UX THING', 'seo_meta_description' => 'If you feel like there's just too much to know and can't keep the pace, join us. We'll make sure you don't miss a UX thing.', 'seo_meta_keywords' => '', 'is_seo_robot_index' => '1', 'is_seo_robot_follow' => '1', 'link_terms_use' => 'https://summurai.com/Blog/summurai-terms-use/', 'board_fabicon' => '', 'board_rss_feed_url' => '', 'is_call_to_action' => '0', 'call_to_action_button_text' => '', 'call_to_action_button_url' => '<X> Summies are waiting for you', 'is_call_to_action_desktop_cta' => '0', 'is_call_to_action_desktop_summy_panel' => '0', 'is_call_to_action_mobile_cta' => '0', 'is_call_to_action_mobile_summy_page' => '0', 'call_to_action_desktop_cta_stats' => '0', 'call_to_action_desktop_summy_panel_stats' => '0', 'call_to_action_mobile_cta_stats' => '0', 'call_to_action_mobile_summy_page_stats' => '0', 'call_to_action_app_btn_text' => 'Get the app', 'call_to_action_app_iphone_link' => '', 'call_to_action_app_android_link' => '', 'call_to_action_navigation_latitude' => '', 'call_to_action_navigation_longitude' => '', 'call_to_action_navigation_googlemaps_link' 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=> '', 'rss_cover_image' => '', 'rss_export_link' => 'https://summurai.com/rss/user-experience-fomo', 'hide_embed_iframe_header' => '0', 'hide_embed_iframe_footer' => '0', 'allow_export_text' => '0', 'allow_export_rtf' => '0', 'allow_export_audio' => '0', 'allow_export_image' => '0', 'allow_export_csv' => '0', 'export_alt_head_foot' => '0', 'export_hide_powerby' => '0', 'export_alt_code' => '', 'crm_type' => '0', 'hubspot_access_token' => '', 'hubspot_client_secret' => '', 'show_reg_company_name' => '1', 'show_reg_job_title' => '1', 'show_reg_scheduling' => '0', 'reg_consent_text' => '', 'from_app' => '0', 'from_embed_playlist' => '0', 'is_blocked' => '0', 'is_deleted' => '0', 'active_date' => '2023-09-27 20:47:48', 'created' => '2019-06-22 09:37:01', 'modified' => '2024-04-24 10:12:59' ) ) $lead_id = (int) 0 $title_for_layout = 'Summy | Experience Design in the Machine Learning Era' $permissions = null $logedin_user_details = null $item_title = 'Experience Design in the Machine Learning Era' $item_summary = 'This article by author Fabien Girardin discusses the duties in his position as a designer at "B.B.V.A. Data and Analytics Company. He assists in the design of systems to provide enhanced user interactions of various software products by using comprehensive machine learning methods with teams of fellow data scientists with the company. What data scientist and designers in his company like others are doing in the new are of machine learning is upgrading their design systems into systems that uses machine learning by use of artificial intelligence, to have software in items like online shopping, banking and decision recommending predict the user's product preferences, selections, and purchase decisions based on data the machines learn from the users. In this article, Mr. Girardin’s goal is to improve on user experience with design systems his company is responsible for. He believes the new practice of providing the enhanced user satisfaction will beneficial for the customer and the company for 3 reasons. The first is that it will create new types of customer interactions. This means as new technology and software comes out, it will be the"systems designers plus data scientists" jobs to work together to create ways for the machine to learn from the user so that data can be gathered to better understand user preferences for example, movie and music genres. In other words, the more the customer uses the machine more the machine learns. The second reason is the evolution of the relationship between the user and the machine. In this article, Alexa is used as an example, and in its marketing it is used to do everything from turning on the lights to ordering pizza. It has a human and machine helper relationship and this is done by machine learning as the user continues to use the Alexa device. The third reason is in the teamwork relationship between the designers and data scientists." In the field of machine learning, "systems designers plus data scientists will be need to work together to create the system. The designers to create the user friendly interface and the data scientists enable the system to learn from the data it collects. In conclusion, the subject of machine learning is clearly explained in this article.' $item_site_name = 'BBVA Data & Analytics' $voice_url = 'https://summarytime.com/uploads/voice_file/7190.MP3' $route_show_url = 'https://summurai.com/'include - APP/View/Article/landing.ctp, line 221 View::_evaluate() - CORE/Cake/View/View.php, line 948 View::_render() - CORE/Cake/View/View.php, line 910 View::render() - CORE/Cake/View/View.php, line 471 Controller::render() - CORE/Cake/Controller/Controller.php, line 954 Dispatcher::_invoke() - CORE/Cake/Routing/Dispatcher.php, line 198 Dispatcher::dispatch() - CORE/Cake/Routing/Dispatcher.php, line 165 [main] - APP/webroot/index.php, line 108
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