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
"We need to bring the same growth mindset we have for design iterations to evolving research operations strategy."
Fatimah RichmondThe Future of ReOps as a Strategic Function: A Roadmap for Getting There
March 25, 2024
"Mobile app crashes give more detailed feedback and user metrics than desktop browser crashes."
Sam ProulxMobile Accessibility: Why Moving Accessibility Beyond the Desktop is Critical in a Mobile-first World
November 17, 2022
"Multimodal design is the design practice you probably don’t realize you’re doing already."
Cheryl PlatzDemystifying Multimodal Design: The Design Practice You Didn't Know You're Doing
April 4, 2024
"Growth as a researcher means expanding your sphere of influence from self to the entire organization."
Dave HoraA Research Skills Evolution
March 10, 2021
"Emotion is the new brand value; trust is earned over time and emotional connection drives loyalty."
Kelly GotoEmotion Economy: Ethnography as Corporate Strategy
May 13, 2015
"Less than 10% of big decisions are based on research, which is rare given how much we spend on it."
Christian MadsbjergInfluencing Strategy
March 31, 2020
"Performance is about how effectively we translate experience into new understanding and turn that into wiser action."
Jen BriselliLearning is the north star: service design for adaptive capacity
November 19, 2025
"Human-centered design can bring curiosity and empathy into traditional analytics work."
Sarah CoyleDesign and Analytics with Sarah Coyle
July 30, 2020
"You can’t collaborate without communicating upfront and sharing context."
Husani OakleyTheme Two Intro
June 6, 2023
