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
"DWI should stand for damn Warren's infertile machine because it was designed on Warren’s typos to solve Warren’s problem."
Samuel ProulxDesigning for Disability, Innovating for Everyone
March 11, 2025
"Service design has a critical role in mapping human and agent interactions in an AI and automation world."
Dharani PereraThe mandala of service design: unlocking alignment and action through service design
November 20, 2025
"The size of your role doesn't determine your impact; it's how your role fits into the bigger picture."
Shahrzad SamadzadehWhat Is My Value? Two Takes and Some Mistakes
January 8, 2024
"Stories are great and build empathy, but how representative are those stories of the broader customer base?"
Ross SmithBreaking Barriers with Empathy
June 9, 2017
"The more participatory the practices, the deeper and more intentional the engagement needs to be."
Alexia Cohen Adriane AckermanIncreasing Health Equity and Improving the Service Experience for Under-Served Latine Communities in Arizona
December 4, 2024
"We need to stop centering ourselves in research and start fighting to pay participants, ensure representation, and use real data."
Jemma Ahmed Megan Blocker Eduardo OrtizRedefining the research toolkit: Expanding methodologies for a changing world
March 12, 2025
"The people here want to be here. They want the company and customers to succeed and to change the world."
Kurdin Bazaz Liz Rytting Alex KarrCulture, DIBS & Recruiting
June 10, 2021
"Net Promoter Score is just one question, but it’s a powerful way to measure loyalty and customer experience."
Julie BaherCulture Change—My Journey
May 14, 2015
"Junk in, junk out. Bad data is worse than no data at all."
Jemma Ahmed Steve Carrod Chris Geison Dr. Shadi Janansefat Christopher NashDemocratization: Working with it, not against it [Advancing Research Community Workshop Series]
July 24, 2024
