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
"I think of a journey map as a suggestion, not a rule, and I might take parts of it to fit the context and people."
Nidhi Singh Rathore Amber DavisEmbracing participation to unlock deeper truths in commercial research
March 12, 2025
"Taking care of ourselves first is essential before we can effectively care for others."
Lada GorlenkoTheme 3: Introduction
June 10, 2021
"For many users with disabilities, mobile is the best experience for the job because of its accessibility affordances."
Sam ProulxMobile Accessibility and You
June 9, 2022
"Multitasking is a myth. You can’t listen and do emails at the same time effectively."
Tutti TaygerlyVideconference: How to Work with Difficult People with Tutti Taygerly
June 25, 2020
"People form habits on software that become efficient, so they naturally resist changes that force new habits."
Paula BachImproving Legacy Software: How Much Better Does it Have to Be?
March 11, 2022
"Not everyone is necessarily asking to be empowered or ready to be empowered when launching these programs."
Trisha TerharEmpathizing with the Empowered: Non-Researcher Responses to Democratization
March 10, 2022
"Only eight people filled out the online application out of over 847,000 pending."
Marina MartinLives on the Line: The Stakes of UX at the Scale of Government
June 14, 2018
"We had an engineering culture that confused uniformity with design, and consistency with design."
Phil GilbertA Consistent Culture of Design
May 14, 2015
"The best way to build a fully accessible foundation is to involve the voices of people with disabilities at every step."
Sam ProulxPrototype Reviews, People With Disabilities, and You
December 8, 2021
