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
"All our components can be data bound so the developer can point to any data source like CMS or JSON to populate UI elements naturally."
George Abraham Stefan IvanovDesign Systems To-Go: Reimagining Developer Handoff, and Introducing App Builder (Part 2)
October 1, 2021
"You earn your seat at the table by championing excellent customer experience and rallying the project team behind it."
Karen PascoeDeveloping Experience Teams and Talent in the Enterprise
June 8, 2016
"How can we find the natural paths that connect myself, my team’s intention, and my business’s intention?"
Dave MaloufTheme 3: Introduction and Provocation
January 8, 2024
"Draw your ideas, draw your ideas — it unlocked a skill I never thought I had and made my ideas have more traction."
Mujtaba HameedFrameworks for Excellence: Using Visual Thinking and Communication to Elevate Your Research
March 26, 2024
"We can get about 80% of the way there from some of our human studies with AI simulations."
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
"The establishment mindset is colonialism manifest in the design world."
Jennifer StricklandAdopting a "Design By" Method
December 9, 2021
"Design shares are more about presenting finished work and practice defending it, not about peer-to-peer feedback."
Joseph MeersmanSweating the Pixel: Scaling Quality through Critique
June 10, 2021
"Who do builders become in this new future AI universe and how do we play together?"
Aletheia DelivreNew Shapes and Emerging Identities for Design Ops
September 11, 2025
"We break down overarching messages into pillar messages and key proof points to create digestible, empathetic communication."
Megan Nipe Lyndsay BoothHuman-Centered Design for Engagement: Maturing from Newsletterville to Personalized, One-to-One Messaging
December 8, 2021
