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
"Design thinking has to be embedded at scale to capture and align creativity across a massive company."
Surya VankaUnleashing Swarm Creativity to Solve Enterprise Challenges
June 10, 2021
"The gap between what the output says and what the intention was is what we're going to call the reasoning gap."
Beth ChappellLeading through ambiguity: Supporting a design team relearning their craft
June 9, 2026
"Combining numbers with rich participant videos and quotes really brings the user to life for stakeholders."
Megan Blocker Amy Bucher Katie Hansen Ricardo Martins Nidhi Singh RathoreDay 2 Theme Panel
March 12, 2025
"The sponge is really full — my brain is pretty full of lots of insights from the last couple of days."
Sarah Auslander Betsy Ramaccia Gordon RossInsights Panel
November 18, 2022
"If something makes you feel unwelcome, the code of conduct explains how to engage with staff to resolve issues."
Bria Alexander Louis RosenfeldWelcome
January 8, 2024
"As designers, we decide what data is required and what data is shared, and that brings responsibility."
Matteo GrattonCan Data and Ethics Live Together?
October 1, 2021
"The team was defunded partly because we didn’t engage enough internally to make the case for our work."
Kara KaneCommunities of Practice for Civic Design
April 7, 2022
"We tested text fields with Mechanical Turk to find which perform best for most people and fed that into specs."
Elizabeth ChurchillExploring Cadence: You, Your Team, and Your Enterprise
June 8, 2017
"Engaging communities means letting go of attachment to our own ideas as designers."
Ashley Cortez Devika Menon Nidhi Singh Rathore Danita J. Reese Mari NakanoShifting Toward Community-Led Innovation in Local Government
December 8, 2021
