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
"Craft means intent and care even in digital products; for example, the bounce animation of Google Maps’ pin shows someone cared."
Uday Gajendar Adam RichardsonFrom AI to Zeitgeist: Theory as the design antidote to AI hype
March 27, 2025
"Instead of overrides, we swap out entire theme files to customize components for different product teams."
Luca RagerEmpowering Gaming at Scale: How Xbox Builds Powerful, Automated, and Distributed Design Systems with Sketch
September 30, 2021
"I wouldn’t want AI to think for me; these tools are to help me grow, not replace my thinking."
Jorge ArangoAI as Thought Partner: How to Use LLMs to Transform Your Notes (3rd of 3 seminars)
May 3, 2024
"The research team is embedded within product squads, working closely with designers, engineers, and data analysts."
Saskia LiebenbergStart Small for Big Impact
May 15, 2019
"Abductive thinking, or what if thinking, is a core capability designers bring to defining what to build and why."
Greg PetroffEverything is About to Change: Software as Material
June 8, 2016
"Thick data is the opposite of big data and essential for rescuing lost context."
Tricia WangFrom Users to Shapers of AI: The Future of Research
March 25, 2024
"A lot of our work still happens in spreadsheets, because they’re flexible and dynamic."
Isaac HeyveldExpand DesignOps Leadership as a Chief of Staff
September 8, 2022
"Showing several concepts side by side and forcing choices yields better discrimination than rating scales."
Michaela MoraAdvanced Concept Testing Approaches To Guide Product Development and Business Decisions
March 11, 2022
"Transparency can be a master source of efficiency when people are trusted to share."
Niko LaitinenAdaptable Org Design for Resilient Times
June 10, 2021