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 want Cisco to lead the industry in standardizing how energy measurement is done for products."
Amy ParnessScaling Sustainability: Complementary strategies that drive long-term success
May 29, 2025
"We reduced the time to approve consent forms from two weeks to minutes through modular content and legal collaboration."
Phil HeskethDesigning Accessible Research Workflows
September 29, 2021
"There are almost two languages in organizations: what we say and what we really mean."
Sofia QuinteroBeyond Tools: The Messy Business of Implementing Research Repositories
March 10, 2022
"Even teams with dedicated designers sometimes have gaps because designers get pulled to other priorities."
Audrey CraneShadow Design–Where Else is Design Happening in Your Organization?
April 20, 2023
"I’ve been using screen readers for 30 years and accessibility has always been a passion of mine."
Sam ProulxSUS: A System Unusable for Twenty Percent of the Population
December 9, 2021
"Only 8% of people we talk to said their stakeholders know how to access results and do so often."
JP Allen Holly HoldenNavigating the UX Tools Landscape
October 1, 2021
"Medical errors should rank as the third leading cause of death in the US, many influenced by software and communication failures."
Mariah HayEthics in Tech Education: Designing to Provide Opportunity for All
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
"Better strategy is continuous, circular, and evolves in real time with every organizational action."
Nathan ShedroffDouble Your Mileage: Use Your Research Strategically
March 31, 2020
