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
"After holding a warm cup of coffee, people perceive others as more trustworthy and engaging."
Daniel GloydDesigning Warmth
February 26, 2025
"Enterprise UX is still nascent—businesses worldwide are just beginning to figure out where UX fits."
Steve Portigal Chris Chapo Kelly Goto Christian RohrerDiscussion
May 13, 2015
"Start with yourself in prioritization, and you’ll likely infect your team and then the organization."
Harry Max Jim MeyerPrioritization for Leaders (2nd of 3 seminars)
June 27, 2024
"Researchers are trained to see power and power imbalances, but seeing our own agency is uncomfortable because it infers responsibility."
Brigette MetzlerScaling ResearchOps: Helping Researchers do Their Best Work
March 30, 2020
"Transparency is building respect for what your teammates do."
Jennifer KanyamibwaCreating the Blueprint: Growing and Building Design Teams
November 8, 2018
"Experimenting with collaboration methods helps you find what works best for your team."
Emilia Åström Jim KalbachUnlock Your Team’s Intelligence with Collaboration Design
June 9, 2022
"Security and great user experience is almost a null set — we need to enlarge that circle."
Christian RohrerInsight Types That Influence Enterprise Decision Makers
May 13, 2015
"If you’re the first researcher in a big org or new domain, focus on defining impact before deciding methodology and partnerships."
George Zhang Molly StevensUX Research Excellence Framework
March 11, 2021
"If you have any tech issues or need customer support, #deogenerall.help is here to assist you throughout the conference."
Bria AlexanderOpening Remarks
September 30, 2021
