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
"The outcome of this method really makes the difference between lightning and a lightning bug."
Anne MamaghaniHow Your Organization's Generative Workshops Are Probably Going Wrong and How to Get Them Right
March 28, 2023
"Bringing our government Partners in early as collaborators and making a clear case for the value of research is one way that we build trust with our stakeholders."
Lisa Spitz Nikki BrandBuilding Trust Through Equitable Research Practices
November 18, 2022
"A useful visual abstraction can’t represent everything; it needs to be selective and targeted."
Mujtaba HameedFrameworks for Excellence: Using Visual Thinking and Communication to Elevate Your Research
March 26, 2024
"There’s no magic solution for digital exclusion; small complementary efforts can make significant impact."
Yalenka Mariën Marie MervaillieDesigning for Digital Inclusion in the Belgian Government
December 8, 2021
"Two-factor authentication accessibility improves when there are customizable options like text, email, or QR codes."
Sam ProulxMobile Accessibility: Why Moving Accessibility Beyond the Desktop is Critical in a Mobile-first World
March 10, 2022
"Solving one task in the design process can generate issues or benefits in different teams."
Patrizia Bertini Alexandra Mengoni LeónPushing DesignOps’ Influence into New Global Markets
September 9, 2022
"The table needs to be larger, longer, or maybe a circle of people without a table to include more voices."
Robert Fabricant Sahibzada Mayed Nidhi Singh RathoreIndustry junctures: Paths forwards for UXR and the critical decisions that get us there [Advancing Research Community Workshop Series]
October 2, 2024
"The LLM gave many different answers for the same passage, each time reporting it was one hundred percent confident."
Trisha Causley[Demo] Complexity in disguise: Crafting experiences for generative AI features
June 5, 2024
"Ideas that get sold without knowing how they work or how they’re made reflect a cultural problem in the team."
Husani OakleyBias Towards Action: Building Teams that Build Work
June 14, 2018
