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
"My Roomba vacuums while I’m at work. It does its work out of my attention."
Christopher NoesselAI of the now: Designing for Agents
July 31, 2024
"If I can't make the actual playbook available, I'm very happy to talk about what it consists of and how we did it."
Maria SkaadenPanel Discussion: Methodologies and Work Environments
November 8, 2018
"Change isn’t disliked as much as the surprise that comes with it—managing expectations makes change more palatable."
Dan Mall“Ask Me Anything” with Dan Mall, Author of Upcoming Rosenfeld Title, Design that Scales
October 2, 2023
"Design always has to take the step forward in some way, whereas product people just go about their day and do their thing."
Dave Malouf Meredith Black Farid SabitovThe Past, Present, and Future of DesignOps: a 2-part DesignOps Community Call (Part 1)
February 17, 2022
"Vanity metrics like membership numbers don’t tell you if the community is healthy."
James LangIf you can design an app, you can design a community
May 22, 2025
"Businesses are stripping down to skeleton crews and canceling anything that doesn't directly drive revenue."
Jon FukudaTheme One Intro
October 2, 2023
"If you care about the solution, you’re much better off hiring hands."
Sol MeszHands or Brains? How to Hire for Strategy, Strategically
January 8, 2024
"There is overlap between design and research when it comes to different types of research; it’s not a strict divide."
Prayag NarulaHow to Empower Your Designers to Do Good Research – And Why You Want To
June 10, 2022
"Don’t bluff regulated healthcare UX experience; everyone will know, and it’s harmful to your team and clients."
Theresa NeilDesigning for Wellness: Specializing in Healthcare
May 22, 2024
