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
"By adopting our account recovery widget, QuickBooks saw a 13% increase in success rate overnight and a 52% reduction in support calls in one year."
Dawn ResselFull-Stack User Experiences: A Marriage of Design and Technology
June 9, 2016
"Yesterday we touched upon topics like chatDBT, chatbots, trust, intentionality, agency, and even alien interns."
Uday Gajendar Louis RosenfeldDay 2 Welcome
June 5, 2024
"Leadership is really important—having an executive who understands the value of the knowledge cycle is key."
Alëna IouguinaDesigning Systems at Scale
November 7, 2018
"Enough content is when your audience feels confident enough to act."
Margot BloomsteinFostering Trust in Your Brand and Beyond
March 12, 2020
"Empathy is an impossibility when there is inherent power imbalance between researcher and participant."
Victor UdoewaRadical Participatory Research: Decolonizing Participatory Processes
March 9, 2022
"Design operations facilitates and magnifies the care that designers can provide to a user's experience."
Angelos ArnisNavigating the Rapid Shifts in Tech's Turbulent Terrain
October 2, 2023
"We measure qualitative sentiment alongside quantitative metrics to design how we support the team."
Amy ThibodeauOpening Keynote: Process and Ambiguity
October 23, 2019
"Share early, share ugly—that’s our mantra for this entire process."
Cassandra PiesterDeveloping and Deploying Your Design Operations Strategy
September 24, 2024
"Only one designer exists for every 43 developers in many large enterprises."
Surya VankaUnleashing Swarm Creativity to Solve Enterprise Challenges
June 10, 2021
