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 have questions, I will take questions throughout."
Kate KalcevichDesigning inclusively with AI
June 5, 2024
"Superfluidity means zero viscosity—flowing without loss of kinetic energy, no friction."
Jacqui FreyFlow and Superfluidity for Design Orgs
November 7, 2018
"Expert intuition can be right or wrong because it relies on cues and heuristics in uncertain contexts."
Kristen Guth, Ph.D.Out of the FOG: A Non-traditional Research Approach to Alignment
March 28, 2023
"One of the best places to start is just learning about assistive technologies: what exists and how they work."
Kate KalcevichIntegrating Accessibility in DesignOps
September 23, 2024
"Younger managers are more alert and understand the importance of measuring meaningful indicators."
Shreya Dhawan Victor Udoewa Xènia Viladas Florian VollmerMaking service tangible: the fastest path to higher performance
November 19, 2025
"If you dive too deep into the subsystems or the bits right away, then your understanding of the whole is going to be lacking."
Sarah FlamionComplex Problem? Add Clarity by Combining Research and Systems Thinking
March 31, 2020
"It’s impossible for any single researcher or manager to identify all cross-functional impact opportunities alone."
Fatimah RichmondThe Future of ReOps as a Strategic Function: A Roadmap for Getting There
March 25, 2024
"Researchers have superpowers: deep listening, empathy, making connections, synthesis — capabilities everyone in business needs today."
Robin BeersResearch as a Catalyst for Organizational Transformation
March 12, 2021
"I encourage you all to ask questions throughout the day and follow up with speakers and cohorts to go deeper."
Ariel KennanTheme 2 Intro
December 9, 2021
Latest Books All books
Dig deeper with the Rosenbot
What is the ROI of involving partners like designers and product managers in user interviews?
What are the challenges and opportunities posed by democratization of user research within organizations?
What does a successful healthcare UX career look like in terms of accumulating influence and aligning with clinical/business goals?