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 prioritize to code switch to clarify value and relevance using language as a tool for meaningful activities."
Shan ShenTranslating UX Terms into Business Contexts
November 29, 2023
"We set up a design-led workshop with all leaders to craft what design-driven meant for our organization, leading to it becoming everyday language."
Louis Rosenfeld Jose Coronado Rachel Posman Guneet Singh Crystal YanThe Bigger Picture: A Panel Discussion
October 23, 2019
"Afrofuturism shows us the future must reckon with the past and center difference and diversity."
Devon PowersImagining Better Futures
March 9, 2022
"We’ve seen about a 12% boost in conversion rates in just a few weeks with some clients using the platform."
Gillian Salerno-Rebic Mark MicheliRedefining Speed and Scale: How Accenture’s GrowthOS Uses AI-Simulated Insights to Reduce Risk and Accelerate Innovation
June 10, 2025
"Co-creation only works when it has boundaries."
Bethany BrownRewiring operations with service design and AI
November 20, 2025
"Only 8% of older adults are using the internet in contrast to 48% of younger adults."
Rittika BasuAge and Interfaces: Equipping Older Adults with Technological Tools
February 23, 2023
"We are obligated to treat each other with kindness and respect; this is a safe and inclusive space."
Bria Alexander Louis RosenfeldWelcome
January 8, 2024
"Next generation tools are being built not for specialists but for the broader population, like product managers and engineers."
James LangHopeful Futures for UX Research
January 30, 2026
"You want someone who writes with equal parts empathy and authority, not just pure authority."
Louis RosenfeldCoffee with Lou: Should You Write a (UX) Book?
March 7, 2024
