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
"From an engineer's perspective, we don't have to talk about the engineering but talk about the value, including usability, and that is what builds trust."
Noreen Whysel Katie SaindonShort Take #4: UX/Product Lessons from Your Industry Peers
December 6, 2022
"Most strategy is done pretty poorly, it's misleading, sloppy, and often ignored after it’s produced."
Nathan ShedroffDouble Your Mileage: Use Your Research Strategically
March 31, 2020
"We consolidated over ten tools into one supply chain management system with a user-led team of 4 UXers, 2 product managers, and 6 engineers."
Anat Fintzi Rachel MinnicksDelivering at Scale: Making Traction with Resistant Partners
June 9, 2022
"Insights are unexpected discoveries about how things work and how to make them work better - Gary Klein."
Johanna KollmannInsights-Driven Product Strategy: Get your Research to Count
December 6, 2022
"Consistency means the same interactions always do the same things; buttons are in the same place, and flows don’t change suddenly."
Sam ProulxOnline Shopping: Designing an Accessible Experience
November 29, 2023
"Empathizing with product managers is a great first step towards turning understanding into bigger influence for product and design."
Aditi Ruiz Christian Crumlish Farid SabitovA PM State of Mind: Empathy Mapping Your Product Manager, Pt. 1
December 6, 2022
"Mapping is not just for presentation; it’s a practice to evolve your understanding of a complex problem space."
Sheryl Cababa Alexis OhThinking in systems to address climate with Sheryl Cababa
June 12, 2024
"Magic eight ball thinking is the risk of blindly accepting AI answers without critical evaluation."
Savina HawkinsHarnessing AI in UXR: Practical Strategies for Positive Impact
March 26, 2024
"X is going to change everything. Sometimes it’s true, and sometimes it’s just hype."
Husani OakleyTheme Three Intro
June 6, 2023
