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
"Visibility is the most vital thing for UX leaders to be focused on."
John Calhoun Rachel PosmanMeters, Miles, and Madness: New Frameworks to Measure the (Elusive) Value of DesignOps
September 24, 2024
"Clear and consistent communication really keeps design leadership accountable and promotes transparency via knowledge sharing."
Kim Holt Emma Wylds Pearl Koppenhaver Maisee XiongA Salesforce Panel Discussion on Values-Driven DesignOps
September 8, 2022
"LLMs don’t have memory, so rating on a scale from one to five is pretty random. Better to have yes or no answers."
Peter Van DijckHands-on AI #2: Understanding evals: LLM as a Judge
October 15, 2025
"Manuel Herrera is a visual thinker and illustrator whose energy always blows me away."
Uday Gajendar Louis RosenfeldDay 2 Welcome
June 5, 2024
"Human-centered design can bring curiosity and empathy into traditional analytics work."
Sarah CoyleDesign and Analytics with Sarah Coyle
July 30, 2020
"If you want to invite a friend or colleague, the sponsor sessions are free and awesome to attend."
Bria AlexanderDay 3 Welcome
September 25, 2024
"There is no need to take notes; session notes, sketchnotes, resource lists, videos, and decks will be shared."
Bria AlexanderOpening Remarks
October 1, 2021
"It’s on each of us to design a process to prevent organizations from overtrusting synthetic insights before real user validation."
Gillian Salerno-Rebic Mark MicheliRedefining Speed and Scale: How Accenture’s GrowthOS Uses AI-Simulated Insights to Reduce Risk and Accelerate Innovation
June 10, 2025
"Connection is the energy between people when they feel seen, heard, and valued without judgment."
Alla WeinbergDesign Teams Need Psychological Safety: Here’s How to Create It
September 8, 2022