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
"I want a story that moves and quakes me, palpitates my heart and shakes me."
Victor UdoewaResearch in the Pluriverse
March 29, 2023
"Digital governance is about who’s supposed to make the decision, not what the decision is."
Lisa WelchmanCleaning Up Our Mess: Digital Governance for Designers
June 14, 2018
"Skills are modular tasks that bots can perform, triggered by specific commands or inputs."
Noz UrbinaRapid AI-powered UX (RAUX): A framework for empowering human designers
May 1, 2025
"Using the inverted pyramid doesn’t mean dumbing down your findings, it means explaining complex issues clearly."
Bruce GillespieLearning from journalism: Balancing impactful communication with compassionate storytelling
March 13, 2025
"Concept testing is about testing ideas before development to confirm there is a real need for the product."
Michaela MoraAdvanced Concept Testing Approaches To Guide Product Development and Business Decisions
March 11, 2022
"I call it proactive research ops because often teams only think about ops when something breaks, but we started early before there was drama."
Saskia LiebenbergStart Small for Big Impact
May 15, 2019
"Throw away the rule book in early ideation phases to get as many ideas out as fast as possible."
Billy CarlsonIdeation tips for Product Managers
December 6, 2022
"Military contractors didn’t have design teams; most had never invested in UX design before."
Michal Anne RogondinoSaving Outer Space: The First UX Design System for Our Nation’s Satellites
January 8, 2024
"Starting meetings with simple icebreakers or sharing pronouns and locations really breaks the ice and builds relationships."
Deirdre Hirschtritt Cesar Paredes Marie PerrotResearch is Only as Good as the Relationships You Build
November 17, 2022
