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
"Small wins are big overall in healthcare because they build credibility and trust for bigger changes."
Eric ShumakeDiagnosis UX: Building Influence in Healthcare Design
April 9, 2026
"Shamus Bern will talk about how to drop into a client site and grok what’s going on quickly without sacrificing why they hired you."
Dan WillisTheme 3: Intro
January 8, 2024
"Unless it’s designed for digital use it’s not going to get widely shared throughout the organization."
Sean DolanA Practical Look at Creating More Usable Enterprise Customer Journeys
October 31, 2019
"If you just throw new hires into cubeville, most of them would probably quit."
Adam CutlerPeople + Places + Practices = Outcomes
June 8, 2016
"Don’t just think about design systems as an artifact but as a living ecosystem connecting people, tools, and products."
Nathan CurtisBeyond the Toolkit: Spreading a System Across People & Products
June 9, 2016
"Most designers feel they understand and care, but many aren’t doing much yet—there’s a disconnect between head, heart, and hands."
Saara Kamppari-MillerInclusive Design is DesignOps
September 29, 2021
"If you see a person standing alone, even if that person’s a CEO, walk up to them."
Michal Anne RogondinoSaving Outer Space: The First UX Design System for Our Nation’s Satellites
January 8, 2024
"Everything else flows everywhere. It’s always going to be about humans at the end of the day, no matter the technology."
Feyikemi AkinwolemiwaUninvited, Not Unwelcome: What Researchers Bring When Nobody Asked
August 19, 2026
"Check the people on the board and the advisors because that tells you a lot about the company’s true values."
Max Gadney Andrea Petrucci Joshua Stehr Hannah WickesAssessing UX jobs for impact in climate
August 14, 2024
