Summary
Designers stand at the verge of a great professional opportunity: artificial intelligence. This technology enables computers to study the world and make predictions using unstructured data. We can speak to machines—and machines can speak back. We can gesture to devices, expressing emotion and intent, and machines can respond meaningfully. We can look to computers not just for interaction, but for companionship. How can designers adapt and thrive in this evolving terrain? How might we map out new brands, platforms and experiences between human and machine? What dangers must we address? What destructive ideologies must we reveal? What possibilities for a better future might we explore and prototype?
Key Insights
-
•
Designers and data scientists have complementary approaches that must collaborate for effective AI experiences.
-
•
Anticipatory design can preempt user needs, creating highly personalized experiences across industries like fast food, retail, and healthcare.
-
•
Users tend to overtrust autonomous AI systems, but their trust can quickly erode when predictions are inaccurate, requiring balanced design strategies.
-
•
Designers should build interfaces that allow users to verify AI outputs and provide feedback in the moment of use.
-
•
Human-AI teaming enables users to manage complex systems and discover insights unattainable alone, as seen in intelligence analysis prototypes.
-
•
Designers must thoughtfully decide which human skills to preserve versus which to automate, informed by metaphors like Elizabeth Churchill's pedal-assist system.
-
•
AI operates as an alien intelligence without understanding consequences, so humans must provide ethical context and oversight.
-
•
Younger designers exhibit enthusiasm but lack critical skepticism; educational programs must nurture a balanced perspective on AI.
-
•
Learning data science vocabulary empowers designers to collaborate effectively with technical teams and shape AI-driven products.
-
•
The emergent field of contestable AI UX patterns highlights the need for interfaces that support in-the-moment user challenges to AI outputs.
Notable Quotes
"Designers need data, but data also needs designers."
"If we don’t collaborate, we’ll end up with a lot of meals that nobody wants to eat and that are really bad for us."
"Anticipatory design means the system serves up what the user needs or desires before they even request it."
"Humans tend to overtrust autonomous systems, like following GPS directions into a lake."
"When the AI prediction is wrong, trust erodes very quickly; balancing skepticism and trust is critical."
"Human and AI teaming can produce insights and wisdom that would be difficult to discover alone."
"The question is not just what to automate, but what human skills do we want to preserve."
"AI is a kind of pedal assist system: sometimes you want it to push you, sometimes you want to pedal yourself."
"AI has no understanding of consequences; humans bring ethical meaning and responsibility."
"Designers must resist manipulation and marginalization inherent in automated systems to envision futures we want to live in."
Or choose a question:
More Videos
"I have to triangulate socially because Yorkshire folks are quite reserved; they won't openly admit issues."
Adam Cutler Karen Pascoe Ian Swinson Susan WorthmanDiscussion
June 8, 2016
"Middle managers are responsible for the how—process, coordination, and communication—and you don’t see the value of that until it’s missing."
Peter MerholzThe Trials and Tribulations of Directors of UX (Videoconference)
July 13, 2023
"Governance isn’t about choking creativity, it’s about putting bounds and clarity that enable purposeful collaboration."
Lisa WelchmanCleaning Up Our Mess: Digital Governance for Designers
June 14, 2018
"The time for action is now, and it must be collaborative."
Vincent BrathwaiteOpener: Past, Present, and Future—Closing the Racial Divide in Design Teams
October 22, 2020
"The greatest sign of success for a teacher is just to be able to say the children are now working as if I did not exist."
Brenna FallonLearning Over Outcomes
October 24, 2019
"Treat identities as elastic, not as fixed personas, because people’s needs and roles are complex and changing."
Tricia WangSpatial Collapse: Designing for Emergent Culture
January 8, 2024
"Creating hypotheses from pain points with measurable success criteria helped prioritize which to pursue."
Edgar Anzaldua MorenoUsing Research to Determine Unique Value Proposition
March 11, 2021
"Context-related data gathered through qualitative research is the first to reach our decision-making centers in the brain."
Designing Systems at Scale
November 7, 2018
"Randomization is magic — it evenly distributes confounds so the only difference affecting results is your change."
Erin WeigelGet Your Whole Team Testing to Design for Impact
July 24, 2024
