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
"This is a story about what happens when you stop trying to will others to think like you and start creating conditions under which both frames can coexist productively."
Kurt McCullochFaster alone, further together: Rebuilding collaboration in the age of AI research
March 10, 2026
"User research is a validation function is a common pitfall, but its real power is uncovering meaningful insights early."
Craig Brookes Andreas Huebner Morgan Quinn"Just Make it Look Good" and Other Ways We're Misunderstood
June 11, 2021
"Organizations contain antibodies—cultural forces that resist change."
Bill ScottLean Engineering: Engineering for Learning and Experimentation in the Enterprise
May 14, 2015
"Data, information, and knowledge are distinct, and we need systems that transform data into actionable context-rich knowledge."
Husani OakleyTheme Two Intro
June 6, 2023
"Culture eats strategy for breakfast—integrating cultures is the hardest and most critical part of mergers."
Jorge ArangoMeeting of the Waters: Designing for Successful Inorganic Growth
August 12, 2021
"Super strength is the resilience and endurance to partner and drive strategic impact backed by customer obsession."
Kate Koch Prateek KalliFlex Your Super Powers: When a Design Ops Team Scales to Power CX
September 30, 2021
"Flexibility is not about using the same methodologies; it’s about moving together towards the same goal."
Sol MeszHands or Brains? How to Hire for Strategy, Strategically
January 8, 2024
"You can’t put the toothpaste back in the tube once biased AI harms your brand or users."
Jay BustamanteNavigating the Ethical Frontier: DesignOps Strategies for Responsible AI Innovation
October 2, 2023
"Constraints are a key element to creativity; without them, design can become ungrounded and chaotic."
Dave Malouf Amy ThibodeauPanel: Design Systems and Documentation
November 7, 2017
