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
"Every job is a climate job. Every job has to be a climate job."
Aiyana Bodi James Christie Marc O'Brien Louis RosenfeldThree Key Climate Initiatives and How You Can Help
September 11, 2024
"You owe it to your organization and your team to grow the practice while staying true to your vision."
Rob Mitzel Sébastien MaloThe Tale of Two Companies: Building a Successful UX Practice in a Century-Old Enterprise
January 8, 2024
"I don’t want a surgeon to tap me on the shoulder and ask where to put the incision; that’s their expertise."
Christopher NoesselAI of the now: Designing for Agents
July 31, 2024
"Traditional journey maps are useless diagrams that don’t capture the real emotional and contextual complexity."
Noz UrbinaRapid AI-powered UX (RAUX): A framework for empowering human designers
May 1, 2025
"The techno-optimist hacker lens believes growth is the only metric that matters, enabling disruption but causing blind spots."
Robin BeersNavigating organizational systems: Rethinking researcher’s role in driving change
March 13, 2025
"The relational dynamics of knowledge are shifting from hierarchical and linear to relational and plural."
Jemma AhmedResearch at an inflection point: Adapting to a new era of collaboration, equity, and innovation
March 11, 2025
"In our desire to see the big table, we often forget that sometimes it’s more important not to seek validation at the table but to offer it to someone else."
Kit Unger Lada GorlenkoTheme 3 Intro
June 10, 2022
"Design research programs are drifting frames for design experiments guided by collective inquiry questions instead of fixed outputs."
Josina VinkNavigating the pitfalls of systems thinking in service design
December 4, 2024
"It’s really great to have no scripted approach because it lets you ask what some might call dumb questions."
Deanna MitchellDesigning with culture: Unlocking impactful insights for Product and UX
March 12, 2025
