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
"Design thinking has to be embedded at scale to capture and align creativity across a massive company."
Surya VankaUnleashing Swarm Creativity to Solve Enterprise Challenges
June 10, 2021
"We’ve worked on news organizations that folded creating content that just went out there into the void."
Patrick Boehler Madison KarasThe service shift: transforming media organizations to create real value through design
November 19, 2025
"Research and design together tell a continuous story that guides what problems we tackle and how we solve them."
Andreas Huebner Amy Takata Craig BrookesWhat Is It Like To Be Part of The UX Team at Compass?
March 11, 2021
"We have a moral duty to bring global conversations back to local communities to increase diversity and richness."
Patrizia Bertini Alexandra Mengoni LeónPushing DesignOps’ Influence into New Global Markets
September 9, 2022
"The Austin studio’s walls can be put up and taken down by a 90-pound person on a Home Depot shopping cart."
Phil GilbertA Consistent Culture of Design
May 14, 2015
"You need a lot of annoying handholding with the AI: reminding it of how many people you have or simple things."
Yulya Besplemennova[Demo] Stress-testing GenAI in user research synthesis
June 4, 2024
"We recruited 50% more than we needed because no-shows are incredible in remote sessions."
Sarah RinkRemote User Research: Dos and Don'ts from the Virtual Field
June 11, 2020
"When clients join us in the field and experience research deeply, they often become advocates who come back for more."
Mujtaba HameedThe new horizon of ethnography: using AI to unlock the full potential of in-person research
March 11, 2026
"One of the challenges for designers is to orient around forces rather than only people."
Sheryl CababaLiving in the Clouds: Adopting a Systems Thinking Mindset
June 6, 2023
