Experience Design in the Machine Learning Era

Notes for designers and data scientists who create together systems that learn from human behaviors

Fabien Girardin
Dec 9, 2016 · 18 min read
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1. Introduction

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 changing.

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2. The new types of user experiences

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.

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Spotify Discovery Weekly explained. Schema adapted from The magic that makes Spotify’s Discover Weekly playlists so damn good.
  1. On the other side, Spotify uses the billion of playlists to build a model of all the music they know about based on all the songs people group into playlists.
  2. Every week it connects the knowledge of music built with each user personal taste profile. Basically, if a favorite song tends to appear on playlists along with a third song not heard before, it will suggest that new song.

2.1. Design for discovery

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.

2.2. Design for decision making

Data and algorithms also provide means to personalize decision making. For instance at D&A we developed advanced techniques to advise BBVA customers on their finance.

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2.3. Design for uncertainty

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 by Patrick Hebron 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.

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The beautiful seams of the Kayak Price Trend algorithm with a confidence level for the purchase advise

“Seamful design involves deliberately revealing seams to users, and taking advantage of features usually considered as negative or problematic”.

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.

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  • The recall score communicates the ability to provide a large set of possible good recommendations.

2.4. Design for engagement

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.

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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.

2.5. Design for time well spent

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.

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2.6. Design for peace of mind

Data scientists are good in detecting normal behavior and abnormal situations. At D&A, 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.

  • Design for conversation
  • Design for automation

3. The new relation between humans and machines

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 engineering”. 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 time”. This description highlights the need to design the experience for systems to learn from human behavior.

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The design for machines to learn. Image inspired by Mike Kuniavsky: The UX of Predictive Behavior for the IoT.
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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. See also Understanding the Lifecycle of Service Experiences by Megan Erin Miller.

“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.”

— 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.


4. The partnership between designers and data scientists

My experience so far envisioning experiences with data and algorithms shows that it is a different practice from current human-centered design. At D&A, 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.

When design meets science

At the moment of shaping an experience, we exploit thick data, the qualitative information that provides insights on people’s lives (see Tricia Wang 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.

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The data science method and its cyclical processes of constant evaluation and refinement

4.1. The touchpoints

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 language”. 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:

  1. Assess any assumption with insights from quantitative exploration, desk research and field research.
  2. Articulate the key questions from the vision and the research. Is the team asking the right questions and are the answers algorithms could give actionable?
  3. Understand all the limitations of the data model that gives answers.
  4. Specify the success metrics for a desirable experience and define them before the release of a test. The validation phase acts as stopping point and it must be defined as part of the objectives of the project (e.g. improve the recall of the recommendations by 5%, detect 85% of customer who are about to default).
  5. Evaluate the impact of the data engine on the user experience. As stated by Neal Lathia, it is particularly hard for data scientists to work “offline” on an algorithm and measure improvements that will correlate with improvements in the actual user experience.

4.2. A vision-driven partnership

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 Backwards’ process as described by their CTO 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.”

Werner Vogels

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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.

“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 D&A, 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.

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5. The design characteristics

In this article, I have argued that, with the advance of machine learning and “artificial intelligence” (AI), it becomes 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 evolution of human-centered design. So far in my journey of creating meaningful experiences in the machine learning era, I can articulate the following characteristics:


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