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
"If you are not speaking to the CEO or C-executives about compliance and transformation, someone else will."
Patrizia BertiniThe (r)evolution of designOps: It’s Time to Think (really) BIG
September 11, 2025
"Whenever someone makes a game accessible today, it’s considered an inspiring hack not a built-in feature."
Sam ProulxTo Boldly Go: The New Frontiers of Accessibility
March 11, 2022
"Good UX is good business."
Product and Design at Bloomberg: A 15-year Evolution
December 6, 2022
"Updating and adopting design system components is company time, so a slower release schedule helps teams keep up."
Charles Lee Jennie YipBuilding a New Home for the Atlassian Design System
October 22, 2020
"At the committed stage, executives start asking what design can do for business strategy."
JJ KercherA Roadmap for Maturing Design in the Enterprise
June 15, 2018
"Avoid strictly timed interactions because they stress everyone, especially people using screen readers or with cognitive disabilities."
Sam ProulxOnline Shopping: Designing an Accessible Experience
October 3, 2023
"Without human interplay, it’s a simulation or model, not a war game."
Terry BuckmanWargaming (An Introduction)
August 10, 2023
"Change is a team sport; meaningful systemic change requires partnerships and advocacy from bottom to top."
Aletheia DelivreNew Shapes and Emerging Identities for Design Ops
September 11, 2025
"When organizations have regime change, shifting high leverage points can cause catastrophic effects, sometimes destructive and unintended."
Erin Hoffman-JohnThis Game is Never Done: Design Leadership Techniques from the Video Game World
November 6, 2017
Latest Books All books
Dig deeper with the Rosenbot
How can designers use AI to create entirely new interaction paradigms instead of just improving productivity?
Why is it important for product teams to focus on questions rather than jumping straight to methods?
What are the benefits and challenges of pairing engineers and designers in small, focused pods for product development?