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
"The research librarian equals consistent taxonomy; without that, you can’t build accessible and searchable knowledge."
Sarah Sgarlato Pierini Kevin NewtonFrom Passion to Execution: A Story of Evolving Research Maturity at LinkedIn
September 9, 2022
"Be present, put devices down, turn off Slack notifications, and really engage."
Shelby SwitzerMaking Space for Community Knowledge-sharing in a Distributed World
December 10, 2021
"Steve Denning says SAFe is codified bureaucracy where the customer is almost totally absent."
Jack MoffettSAFe or Sorry?
May 29, 2019
"We pay all of our speakers and provide both subject matter and professional speaker coaching to polish your presentation."
Rachael Dietkus, LCSW Victor Udoewa Jennifer StricklandEverything You Need to Know about the Civic Design 2022 Call for Presentations
May 17, 2022
"You can’t expect people to comply with standards if you don’t know who they are."
Lisa WelchmanCleaning Up Our Mess: Digital Governance for Designers
June 14, 2018
"Sentient design is about intelligent interfaces that are aware of context and intent so they can be radically adaptive to user needs in the moment."
Josh Clark Veronika KindredSentient Design: New Postures for AI-Mediated Experiences (2nd of 3 seminars)
January 29, 2025
"My screener automatically reads out all incoming messages into my headphones, so I hear my voice, the Zoom messages, the speaker notes, and the slides all at once."
Sam ProulxOnline Shopping: Designing an Accessible Experience
November 29, 2023
"Scalability is a concern; this direct calling approach works best in early-stage products with smaller user bases."
Prayag Narula Abhinav KrishnaDialing for Research: How to Reach the Unreachable
March 10, 2022
"Mobile screen readers can confirm basic accessibility but desktop testing is still needed due to differences across tools."
Sam ProulxMobile Accessibility and You
June 9, 2022
