Every month in my column Ask UXmatters, our panel of UX experts answers readers’ questions about a broad range of user experience matters. To get answers to your own questions about UX strategy, design, user research, or any other topic of interest to UX professionals in an upcoming edition of Ask UXmatters, please send your questions to: [email protected].
The following experts have contributed answers to this month’s edition of Ask UXmatters:
- Pabini Gabriel-Petit—Principal Consultant at Strategic UX; Publisher, Editor in Chief, and columnist at UXmatters; Founding Director of Interaction Design Association (IxDA)
- Adrian Howard—Generalizing Specialist in Agile/UX
- Janet Six—Product Manager at Tom Sawyer Software; UXmatters Managing Editor and columnist
- Andrew Wirtanen—Lead Designer at Citrix
Q: Do you have experience doing UX design for an AI application? How is it different from working on a traditional application?—from a UXmatters reader
“For the most part, it isn’t different,” answers Adrian. “All of the core UX design tasks—understanding the people who would use the product, their objectives, their painpoints, and the ways the product can help them—remain the same.
“The very fact that some folks think AI projects are significantly different from other UX design projects is actually one of the danger points. Some teams get carried away by the technology and start thinking the AI capabilities alone are going to persuade people to use the product. But they won’t—unless the product actually solves customers’ problems effectively.
“If the AI doesn’t solve the customer’s problems well, that truth can be hard for the rest of the organization to hear—especially if they’ve placed all their bets on their product’s AI capabilities. So you’ll need to use all your facilitation and persuasion skills to ensure that the outcomes the customer wants are front and center throughout the product-development lifecycle.”
The Role of User Experience in Creating AI Applications
“When designing an AI application, the UX designer’s role of user advocacy takes on greater importance than ever before,” advises Pabini. “Some key contributions that User Experience can make to an AI product-development project include the following:
- conducting generative user research—By conducting user research, UX researchers and designers can ensure that a product team develops a deeper understanding of the audience for an AI application, as well as the needs and behaviors of potential users. Their research findings reflect the human experience and provide the reliable qualitative data that should be at the foundation of any AI application. Gaining this understanding can also help reduce product-team members’ inherent bias and ensure that the team chooses the right training data for machine-learning algorithms.
- defining training data—The experience information architects and UX designers have of defining metadata for information on the Web translates quite well to defining the training data for machine-learning algorithms. In addition to leveraging quantitative data, training data should rely on learnings from qualitative user research. Information architects and UX designers are accustomed to creating design solutions that are based on findings from user research and appreciate their value. A machine-learning application is only as good as its training data.
- gaining users’ trust—As user advocates, UX professionals understand that it is essential to gain users’ trust—when designing any application, but especially for an AI application. Design solutions that offer transparency and give users control over their personal data make it much more likely that users will trust an organization enough to provide the data that drives many AI applications—especially the machine learning that supports personalization.
