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
"Empathy is not a single thing but a combination of intellectual understanding, emotional resonance, and compassionate action."
Victor UdoewaRadical Participatory Design: Decolonizing Participatory Design Processes
December 10, 2021
"Every project has a financial job to be done, whether it’s impacting revenue, reducing churn, or cutting costs."
Giff ConstableFinancial fluency for product leaders: AMA with Giff Constable
April 11, 2024
"Investing in teaching from the ground up helps grow the design ops discipline for the future."
Laine Riley Prokay Lisa GordonCarving a Path for Early Career DesignOps Practitioners
September 9, 2022
"If rules and stats update in real time, you could know when saturation is reached as data streams in."
Vitorio MilianoDon’t call it AI: Turn words into numbers with quantitative ethnography
March 11, 2026
"You can’t make anyone else care. We’re asking the wrong question."
Sarah BarrettThe "How" of Enterprise Information Architecture
June 6, 2023
"Qualitative researchers can help design better human-in-the-loop evaluation guidelines that consider social context."
Prayag Narula Rida QadriHCI 2.0: Humanity Deserves the Attention that UX Research has to Offer
March 28, 2023
"Synthetic duplicates living on after data deletion raise ethical questions about users’ right to be forgotten."
Llewyn Paine[Demo] Deploying AI doppelgangers to de-identify user research recordings
June 5, 2024
"Today is about empowerment and positivity at scale."
Kit UngerTheme 1 Intro
June 8, 2022
"Companies with high corporate design maturity perform better financially and have happier employees."
Kit UngerTheme 2: Introduction
June 10, 2021
