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
"Having a designated buddy for new hires proactively soliciting questions creates a supported partnership and eases onboarding."
Julie Gitlin Esther RaiceDesign as an Agent of Digital Transformation at JPMC
June 9, 2021
"Our presenters have iterated on their talks to provide rich resources and new knowledge."
Ariel KennanTheme 2 Intro
December 9, 2021
"The LAUSD stakeholder felt armed with data and designs to articulate future benefits in a language the school board could understand and quantify."
JD BuckleyCommunicating the ROI of UX within a large enterprise and out on the streets
June 14, 2018
"Inclusion and accessibility need to be part of every designer's role, even if today it is one person's job."
Reginé GilbertAsking the Right Questions: Life, Hope and Moving Forward During the Pandemic
June 10, 2022
"We have too many privacy lawyers and not enough consumer lawyers, which clouds enforcement priorities."
Harry Brignull Mark Leiser Robert StribleyBeyond Clicks and Tricks: Why deceptive design has grown into a regulatory faultline
January 16, 2026
"Having DPMs report separately from design managers creates a therapist-like safe space for designers to share issues."
Brennan HartichCommunicating and Establishing DesignOps as a New Function
November 7, 2018
"Most strategy is done pretty poorly, it's misleading, sloppy, and often ignored after it’s produced."
Nathan ShedroffDouble Your Mileage: Use Your Research Strategically
March 31, 2020
"You need to know tomorrow’s user as well as today’s because their characteristics differ greatly according to diffusion of innovation."
Christian RohrerResearch Operations at Scale
November 7, 2017
"Give teams permission to think about the whole service and solve the problem holistically."
Kristin TaylorBuilding Bridges Across Organizational Silos
November 18, 2022
