This video is featured in the AI and UX playlist.
Summary
Artificial intelligence (AI) has graduated from science fiction to commoditized widget form, readily able to snap into many processes of daily life. Hence, enterprises of all maturity levels are increasingly eager to explore AI’s roles in their innovation, or outright survival strategies. Concurrently, ethical and responsible development and execution of AI-based solutions will increasingly become critical for purposes of safety and fairness. Ensuring that AI proliferates along the right path will require the infusion of multi-faceted research activities along the entire AI lifecycle. We will discuss the challenges and opportunities regarding this topic in this presentation.
Key Insights
-
•
70% of enterprise AI projects show little to no business impact, and nearly 90% of data science projects fail to reach production.
-
•
AI bias, particularly intersectional bias in facial recognition, remains a critical unresolved challenge, exemplified by the Gender Shades project.
-
•
Black-box AI decision-making hinders stakeholder trust and adoption, due to AI’s probabilistic nature and complexity.
-
•
AI development teams are overly engineer-centric, lacking inclusion of product researchers and ethicists to address societal and user-centered concerns.
-
•
ML ops, adapted from DevOps, offers governance and accountability frameworks but currently remains engineer-focused.
-
•
Expanding ML ops to include human-centered researchers can improve AI explainability, trustworthiness, and fairness.
-
•
Visualization research is essential to analyze and interpret high-dimensional AI data and uncover hidden biases across intersectional subgroups.
-
•
AI explainability requires moving beyond feature importance towards causal reasoning and natural language explanations accessible to non-technical stakeholders.
-
•
AI trust is evolving and hinges on AI’s ability to provide convincing, interpretable answers that humans can understand and scrutinize in dialogue form.
-
•
Humanizing AI is not simply building human-like interfaces but creating governance frameworks that democratize responsible AI development.
Notable Quotes
"AI development is often uninformed and hurried, resulting in deployments that don’t operate well in the real world."
"Humanizing AI means creating governance frameworks that involve a broad array of research competencies for democratizing safe and effective AI."
"Almost 90% of data science projects do not make it into production—they die on the vine."
"Black box decision making is a hallmark problem—information goes in, something comes out, but we have no clue why."
"Bias is fueled by over-engineering without enough participation from non-technical roles that could reduce it."
"The Gender Shades project exposed how facial recognition algorithms had up to a 33% error rate disparity between demographic groups."
"ML ops offers governance, accountability, and a clear stakeholder responsibility framework borrowed from DevOps."
"We want to increase trust and engagement among end users by helping non-technical stakeholders participate in model evaluation."
"Explainability metrics like trustworthiness and understandability are hard, open research problems needing AI-HCI collaboration."
"AI trust will grow when AI can provide back-and-forth justifications like a human would in conversation."
Or choose a question:
More Videos
"We can design the experience of working with us just like we design for end users."
Christian Crumlish Wendy Johansson Rich Mironov Aditi Ruiz Adam ThomasAfternoon Insights Panel
December 6, 2022
"Matching is very much an art, not a science; there are no hard and fast rules for what counts as a good match."
Joshua NobleCasual Inference
October 6, 2023
"Start with yourself in prioritization, and you’ll likely infect your team and then the organization."
Harry Max Jim MeyerPrioritization for Leaders (2nd of 3 seminars)
June 27, 2024
"China blew up one of their satellites with a missile, creating tons of debris, proving they can threaten our satellites."
Michal Anne RogondinoSaving Outer Space: The First UX Design System for Our Nation’s Satellites
January 8, 2024
"If we release new features too early without alignment, it doesn’t make sense to people and causes confusion."
Matt StoneScaling Empathy, A Case Study in Change Management
June 11, 2021
"Without automation, none of these tools are talking to each other, and operational overhead for your Ops and DPM teams is actually absurd."
Candace MyersStandardizing Design at Scale
September 9, 2022
"There’s this kind of Collective unconscious within teams that formed over the pandemic which is deeper than any other team experience I’ve had."
DesignOps and The Great Talent War of 2021
August 19, 2021
"Friction isn’t just annoying; it’s a force that reshapes behavior and can slow or stop user flow."
David SternbergUncovering the hidden forces shaping user behavior
July 17, 2025
"Connection means feeling seen, heard, and valued; that’s what creates psychological safety."
Alla WeinbergDesign Teams Need Psychological Safety: Here’s How to Create It
September 9, 2022
