Humanizing AI: Filling the Gaps with Multi-faceted Research
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
"Reading unfamiliar news content helps you see structure and language more clearly than reading what you already care about."
Shazia Ali Bruce Gillespie Joyce Lee Andy WarrCommunication: Innovative techniques for making your voice heard [Advancing Research Community Workshop Series]
August 21, 2024
"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
"Auto captions on YouTube struggle with accents and technical terms, so manual or human-reviewed captioning is essential for accuracy."
Sheri Byrne-HaberThe Importance of Accessible Design Systems
January 8, 2024
"Our job is to help others do great work, not to create perfect components for our own ego."
Dan DonaldDesign Systems as a Vehicle for Systemic Change
June 1, 2023
"Carbon removal is necessary to hit climate goals because reducing emissions alone won’t be enough."
Marissa Cui Rachel He Michael Leggett Manos SaratsisClimate Design Product Showcase
March 13, 2024
"Research operations should move from execution facilitators to key players in research strategy and metrics."
Megan Blocker Lada Gorlenko Fatimah Richmond Molly StevensWhat UX research maturity looks like and how we get there [Advancing Research Community Workshop Series]
November 9, 2023
"There’s no faking it with this crowd."
Doug PowellClosing Keynote: Design at Scale
November 8, 2018
"This program has become the gateway drug for customer research for people who didn’t know its power or value before."
Melissa Schmidt Adam MenterHow UX Research Hit It Big in Las Vegas
June 4, 2019
"The three-in-the-box model means having marketing, engineering, and UX leaders working collaboratively."
David Cronin Uday Gajendar Peter Morville Kendra ShimmellDiscussion
May 13, 2015
