Humanizing AI: Filling the Gaps with Multi-faceted Research
This video is featured in the AI and UX playlist.
Summary
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
"High status is taking up more space, holding eye contact, and purposeful gestures."
Cheryl PlatzCollaborative Creativity through Improv
November 7, 2018
"When you tell the truth, especially hard truths, how you say it matters as much as the truth itself."
Melissa Eggleston Maya Israni Florence Kasule Owen Seely Andrea SchneiderPractical People Skills for Building Trust on Teams and with Partners
December 9, 2021
"The prototype caught fire because the motor and axle were misaligned — my design didn’t consider the physical realities."
Dan WillisEnterprise Storytelling Sessions
June 8, 2016
"This is probably the biggest change we are going to see within our working careers."
Robert Fabricant Sahibzada Mayed Nidhi Singh RathoreIndustry junctures: Paths forwards for UXR and the critical decisions that get us there [Advancing Research Community Workshop Series]
October 2, 2024
"Non-designers can build wireframes really quickly using easy drag-and-drop components from a design system."
Billy CarlsonTips to Utilize Wireframes to Tell an Effective Product Story
June 6, 2023
"It’s the innovators who can listen to what is unsaid where research has the biggest impact."
John MaedaMaking Sense of Enterprise UX
June 9, 2016
"The Ministry of Culture in Mexico is creating spaces and policies for designers to solve government challenges using design thinking."
Charlotte LeeTheme 1 Intro
December 8, 2021
"Picking the wrong size image can impact customer experience as much as what you see on screen."
Erin WeigelUX Lessons from running more than 1,200 A/B Tests
July 10, 2024
"We cannot practice women-centricity without including an active role for men."
Mansi GuptaWomen-Centric Research: What, Why, How
March 29, 2023
