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
"If you’re spending more time talking about titles, you’re not actually doing the design work."
Adam Cutler Karen Pascoe Ian Swinson Susan WorthmanDiscussion
June 8, 2016
"You can’t have good AI without good information architecture."
Peter Van DijckDesigning AI-first products on top of a rapidly evolving technology
June 10, 2025
"The biggest mistake in communication is the illusion that it has happened."
Janaki KumarInnovate with Purpose
June 14, 2018
"We have many breaks, and sponsor sessions usually take place during those."
Bria AlexanderOpening Remarks
June 10, 2021
"You have to pick your battles with stakeholders—fight the ones that really matter and let smaller issues slide."
Megan Blocker Amy Bucher Katie Hansen Ricardo Martins Nidhi Singh RathoreDay 2 Theme Panel
March 12, 2025
"Public sector work is about broadening your reach and delivering outcomes to everyone, not winning market share."
Louis Rosenfeld Lashanda Hodge Senongo Akpem Chris HodowanecBecoming a Civic Designer: Making the Move from Private to Public Sector
November 17, 2022
"AI has as much capacity to do as much harm as it does good."
Dr. Jamika D. Burge Nick Fine Alexandra Jayeun Lee Greg Nudelman Bo WangHow UX researchers can partner with (and not be replaced by) AI [Advancing Research Community Workshop Series]
August 31, 2023
"We are all designers with a capital D. That’s how I unify the team and reduce confusion."
Jose CoronadoFrom Zero to Hero
September 8, 2022
"Retention is so important as a leader; you have to make sure people are growing even if they don’t ask for it."
Jaskiran KangWhy Community is Key to Professionalizing Design
October 28, 2022
