Humanizing AI: Filling the Gaps with Multi-faceted Research
This video is featured in the AI and UX playlist.
Summary
Artificial intelligence (AI) has graduated from science fiction to commoditized widget form, readily able to snap into many processes of daily life. Hence, enterprises of all maturity levels are increasingly eager to explore AI’s roles in their innovation, or outright survival strategies. Concurrently, ethical and responsible development and execution of AI-based solutions will increasingly become critical for purposes of safety and fairness. Ensuring that AI proliferates along the right path will require the infusion of multi-faceted research activities along the entire AI lifecycle. We will discuss the challenges and opportunities regarding this topic in this presentation.
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
"Starting a repository for your existing data, however simple, is critical to enable efficient secondary research."
Cara Maritz Rachel NyThe Art of Extrapolation
March 29, 2023
"Protecting my time is something everyone is empathizing with right now."
Ariel KennanCivic Design in 2022
January 13, 2022
"Putting on your oxygen mask first is a metaphor for self-care we often neglect in professional life."
Lada GorlenkoTheme 3: Introduction
June 10, 2021
"Our library includes thousands of hours of videos created by over eleven hundred people."
Louis RosenfeldAbout the Rosenverse
March 15, 2026
"No wiki in the world is as good as somebody who can come and tell you how to bend the rules a little bit."
Gretchen AndersonScaling the Human Center
June 8, 2017
"Sometimes business context points us to a direction where we no longer have the safety of our identity constructs."
Aletheia DelivreNew Shapes and Emerging Identities for Design Ops
September 11, 2025
"Design systems weren’t built until inconsistent experiences across teams made it undeniable."
JJ KercherA Roadmap for Maturing Design in the Enterprise
June 15, 2018
"Product managers are trying to keep up with deadlines but often launch hard-to-use features hoping to make them better later."
Aditi Ruiz Christian Crumlish Farid SabitovPulse Check: Empathy Mapping Your Product Manager, Pt. 2
December 6, 2022
"The traditional product metrics focus narrowly on the product or near-term impact but fail to capture what success means for the whole person."
Raven VealDark Metrics: Illuminating the Negative Impact of Digital Health Design
March 12, 2021
