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
"Offering participants food or shelter crosses professional boundaries and can create unsafe expectations."
Jane Reid Janice HannawaySelf-care in User Research
April 2, 2020
"You have to be merciless in your prioritization when everything feels urgent."
World Usability Day Panel Discussion
November 10, 2022
"Researchers can overcome reluctance to include others by training them closely and collaborating on synthesis to maintain data quality."
Dr Chloe SharpUsing Evidence and Collaboration for Setting and Defending Priorities
November 29, 2023
"Tags shouldn’t change very often; there should be governance to keep things consistent and relevant."
Tony TurnerCapturing Deep Insights
September 30, 2021
"Co-regulation means someone who is safe can help bring another out of a triggered state."
Alla WeinbergDesign Teams Need Psychological Safety: Here’s How to Create It
September 9, 2022
"Design always has a seat at the table at Pinterest because our co-founder is a designer."
Meredith BlackScaling Design Culture
November 6, 2017
"We can use mental model skylines with thinking styles to find support gaps we didn’t see before."
Indi YoungThinking styles: Mend hidden cracks in your market
January 8, 2025
"Although I turned off the AI feature at Config, I started evaluation with security, privacy, and legal teams for governance."
Changying (Z) ZhengNavigating Innovation with Integrity
September 25, 2024
"Service design stops being just a support function; it becomes the strategic driver that transforms organizational performance."
Tamara KartoziiaThink global, adapt local: how service design accelerated B2B market entry by 6 months
November 20, 2025
