Humanizing AI: Filling the Gaps with Multi-faceted Research
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
"Balancing the right versus the right now is key when moving things forward under urgency."
Harry Max Jim MeyerPrioritization for Leaders (2nd of 3 seminars)
June 27, 2024
"We had no budget, we had no backing, and it was a huge success."
Jennifer KanyamibwaCreating the Blueprint: Growing and Building Design Teams
November 8, 2018
"Researchers can publish directly without intermediation to avoid blocking knowledge sharing."
Matt DuignanHITS, Microsoft's internal human insight system: From research library to living body of knowledge
July 16, 2019
"Product and brand are two sides of the same coin, tied together through cultural trends."
Christopher GeisonTheme 1 Intro
March 25, 2024
"We want many voices and perspectives represented in this community and in the book — no contribution is too small."
John Calhoun Rachel PosmanBring your DesignOps Story to Life! The Definitive DesignOps Book Jam
October 3, 2023
"Increasing page load time by three seconds can nullify all your design improvements."
Erin WeigelUX Lessons from running more than 1,200 A/B Tests
July 10, 2024
"Correlation does not equal causation; we're spotting patterns, not proving them."
Marc FonteijnFirst Insights from the 2025 Service Design Salary(+) Report
December 4, 2024
"Minimize anthropomorphism in AI design so users know they are engaging with machines, not humans."
Ovetta SampsonManaging the Human Engagement Risks of AI
June 10, 2025
"Most people don’t come to work to make their own life harder or work on a bad product."
Marc Rettig Julie Baher Phil Gilbert Nathan ShedroffDiscussion
May 14, 2015
