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
"Outside of our keyholes lies information we’re completely oblivious to that can come to haunt us."
Dan WillisEnterprise Storytelling Sessions
June 3, 2019
"Design operations in English sounds cool and exciting, but in local communities things are struggling more."
Patrizia Bertini Alexandra Mengoni LeónPushing DesignOps’ Influence into New Global Markets
September 9, 2022
"Respect your design system rules to ensure consistent experiences across your product and teams."
Jaime CreixemsBest Practices when Creating and Maintaining a Design System
June 7, 2023
"Women are often treated as a minority, which sets them up to be forgettable, dispensable, and ignorable."
Mansi GuptaWomen-Centric Research: What, Why, How
March 29, 2023
"Having engineers watch research clips often leads to faster fixes because they feel the pain firsthand."
Nicole Wright Ned DwyerDemocratizing Research at HoneyBook
March 9, 2022
"The developer who refused to run experiments bragged about success, but it was actually a flop once we ran the test properly."
Steve Sanderson Alissa Briggs Jeff Gothelf Bill ScottDiscussion
May 14, 2015
"We went from a team of 80 to 170 overnight, and that was a wide open cheeks flushed moment for me."
Isaac HeyveldExpand DesignOps Leadership as a Chief of Staff
September 8, 2022
"Data is a language you need to learn to speak and a mindset that must be continuously practiced."
William Newton Jenny ChangHow to Lead With Data, and Without Data
June 7, 2023
"If the scale delivers bad news, we jump on and off to check; if it delivers good news, we accept it quickly."
Sara LogelYour Colleagues are Your Users Too
March 29, 2023
