Humanizing AI: Filling the Gaps with Multi-faceted Research
This video is featured in the AI and UX playlist.
Summary
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've worked with speakers for months to prepare our conference content, so it's expensive to produce, but really good."
Louis RosenfeldAbout the Rosenverse
March 15, 2026
"Growing a team of design ops managers that can fire on all these cylinders all at once."
Jon FukudaTheme One Intro
September 8, 2022
"Design is not dead; the things we know how to do still really matter."
Anne CanteraThe New Design Stack: The Skills Traditional Designers Need to Add To Their Toolboxes ASAP
July 29, 2026
"Research is seen as an adjunct to design, often missed in road mapping and strategic planning."
Renee BouwensLanding Product Impact: Aligning Research as a Foundational Driver for Delivering the World’s Best Products
December 15, 2023
"The alternative to good design isn’t no design, it’s bad design."
Audrey CraneShadow Design–Where Else is Design Happening in Your Organization?
April 20, 2023
"As your design practice matures, your design ops team will reach inflection points, and it’s okay to flip the coin and explore the other side."
John Calhoun Rachel PosmanTwo Sides of the DesignOps Coin: Teams Ops and Product Ops
January 8, 2024
"We can’t just rely on managers to do everything for career growth because managers change too often."
Peter MerholzThe Mysterious Case of the Missing UX Career Path
August 25, 2022
"There is no need for you to take notes because session notes, videos, and decks will be shared as soon as possible."
Bria AlexanderOpening Remarks
November 17, 2022
"Time is the only resource we cannot replenish. Make sure you don’t spend it doing what’s not meaningful."
Anna Avrekh Dr. John Pagonis Klara Pelcl Sina SchreiberExpert Panel: Leading in and with Research
March 10, 2022
