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
"A smaller agile team may require less documentation but has less scope and velocity impact."
Chris HodowanecAgile + User Experience: How to navigate the Agile landscape as an UX Practitioner
November 16, 2022
"We basically wrote an HCD book with 25,000 words of notes for the training workshop."
Elena Naids Liza McRuerThe Power of Difficult Conversations: A Case Study on How We Introduced Design Ops in the Federal Government Space
October 2, 2023
"Service design stops being just a support function; it becomes the strategic driver that transforms organizational performance."
Tamara KartoziiaThink global, adapt local: how service design accelerated B2B market entry by 6 months
November 20, 2025
"Transparency is one of our key values, so everyone in the studio was involved in building and maintaining the evaluation system."
Ignacio MartinezFair and Effective Designer Evaluation
September 25, 2024
"You need to demonstrate compelling reasons why they should work with you and hook your champions and advocates."
Noel LambCultivating Business Partnerships to Grow Research Ops
March 21, 2022
"Our responsibility is to help make digital channels work really well and in a humane, appropriate way."
Leah BuleyClosing Plenary: The Crisis of Digital
March 31, 2020
"If it doesn’t work for you, it doesn’t work."
Marisa BernsteinIt Takes GRIT: Lessons from the Small, but Mighty World of Civic Usability Testing
December 9, 2021
"Human centered design Ops is about making sure nobody thinks they’re a robot."
Candace MyersStandardizing Design at Scale
September 9, 2022
"The unsung hero of Enterprise UX isn’t the user or the researcher or the designer — it’s the person drowning in emails, Excel sheets, and project plans."
Dan WillisEnterprise Storytelling Sessions
June 8, 2016
Latest Books All books
Dig deeper with the Rosenbot
What are the emotional and physical health impacts after leaving a high-pressure UX leadership role?
How can public sector funders shift their innovation investments to align better with strategic priorities?
How can one service designer drive AI integration in an organization lacking a design team?