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 want to foster a partnership between researchers and AI to enhance human expertise, not supplant it."
Taylor KlassmanThe Evolution of UX Research Platforms
March 10, 2026
"I’d argue you couldn’t be effective in any role without some sense of how to create partnerships and move forward."
Frank DuranPartnership Playbook: Lessons Learned in Effective Partnership
January 8, 2024
"All session notes and resources will be available within the hour, so you can just relax and absorb the content."
Bria AlexanderOpening Remarks
September 30, 2021
"If diversity alone made research inclusive, then police could never be anti-black because they include black officers."
Victor UdoewaBeyond Methods and Diversity: The Roots of Inclusion
March 26, 2024
"We lacked the insight to interpret what we were seeing in ways that were culturally relevant in those particular markets."
Joi FreemanA New Vantage Point: Building a Pipeline for Multifaceted Research(ers)
March 30, 2020
"I ask how a goal could go wrong and who might be harmed to help design safer, more effective goals."
Christian Crumlish Aditi Ruiz Johanna Kollmann Catt SmallMorning Insights Panel
December 6, 2022
"Failure is when effort did not produce the desired result, not putting in the effort in the first place."
Dan WardFailure Friday #1 with Dan Ward
February 7, 2025
"These intelligent interfaces are collaborative, proactive partners on the user’s journey."
Josh Clark Veronika KindredSentient Design: New Postures for AI-Mediated Experiences (2nd of 3 seminars)
January 29, 2025
"The end of history illusion is that we think we're done evolving, but we vastly underestimate future change."
Dean BroadleyNot Black Enough to be White
January 8, 2024
