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
"Lived experts are the most innovative voice in the design process, bringing unique and practical solutions."
Samuel MartinCo-Design vs Faux-Design: Navigating the Complexities of Sharing Power in Co-Design
March 27, 2026
"If you want to participate in the conference’s general vibe and community, it is essential to join Slack."
Bria AlexanderOpening Remarks
March 29, 2023
"People with disabilities often won’t ask for help; they simply go to an accessible competitor instead."
Sam ProulxOnline Shopping: Designing an Accessible Experience
March 28, 2023
"The tools are meeting people where they’re already going: ChatGPT or Gemini, rather than forcing new platforms."
Mike OrenImproving Democratized Research with CustomGPTs and Gems
February 25, 2026
"There is no one right way to do things; this is deeply contextual work."
Matt LeMayYou Don’t “Get” Anyone to Do Anything
December 6, 2022
"There is no end state in design ops; it’s a constant state of evolution."
Dianne QueReal Talk: Proving Value through a Scrappy Playbook
October 23, 2019
"Can we build AI systems that foreground social fabric and enable civil conversations around shared resources?"
Dan HillDesigning for the infrastructures of everyday life
June 4, 2024
"You can’t manage everybody the same way; different personalities require different approaches."
Amy Jiménez Márquez Michael J. Metts Joie ChungThe Atypical UX Manager Path
July 23, 2020
"The first impression is crucial as it triggers excessive unconscious processes that influence preferences."
Daniela Magaña Flores Ariane Rahn Jeff Ephraim BanderAhead of Competition: Learn What UX Benchmarking Can Do for Your Business Today
March 10, 2022
