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
"Designing as a team means centering all the humans involved, not just the users."
Christopher Taylor Edwards Valerie RoskeDesign as a Team Practice, A Practical Guide to Cross-functional Collaboration
September 30, 2021
"UX now has a seat at the table — maybe not next to the CEO, but definitely in the room — thanks in large part to the design system."
Nalini P. KotamrajuAn Organizational Story: Salesforce Lightning Design System
June 9, 2016
"The lack of consistency between healthcare IT systems causes critical information delays and interrupted care."
Lais de Almeida Maria IzquierdoDesigning Data Services
December 4, 2024
"Our users still rely on email, Excel spreadsheets, pen and paper, and even post-it notes for documentation."
Laureen Kattan Julie KimCentering Patients and Clinicians in a Complex Government Ecosystem
November 29, 2023
"The community is a big tent - welcoming diverse perspectives and geographies to define and shape civic design."
Rachael Dietkus, LCSW Victor Udoewa Jennifer StricklandEverything You Need to Know about the Civic Design 2022 Call for Presentations
May 17, 2022
"Treat unemployment as a full-time job; be relentless and turn over every stone to improve your chances."
Corey LongHiring in DesignOps: A Critical Study on How to Hire and Get Hired
September 23, 2024
"We can't track actual benefit uptake after users click through to official government sites, only clicks and visits."
Alan Williams Rose DeebDesigning essential financial services for those in need
February 10, 2022
"If we think disability as a mismatch between user and environment rather than a medical problem, it becomes easier to understand how accessibility benefits everyone."
Samuel ProulxFrom Standards to Innovation: Why Inclusive Design Wins
September 10, 2025
"Model companies post-train models on capabilities by showing them millions of good and bad examples."
Peter Van DijckBuilding impactful AI products for design and product leaders, Part 1: The new product journey
July 16, 2025
