The Road to Accountable AI

• Mitch Kapor: AI Gap-Closing

  Legendary entrepreneur and investor Mitch Kapor draws on his decades of experience to argue that while AI represents a massive wave of disruptive innovation, it also represents an opportunity to avoid mistakes made with social media and the early internet. In this episode, he contends that technologists tend toward over-optimism about technology solving human problems while underestimating downsides. Self-regulation by large AI companies like OpenAI and Anthropic is likely to fail, he suggests, because incentives to aggregate power and wealth are too strong, requiring external pressure and oversight. Kapor explains that his responsible investing approach at his venture capital firm, Kapor Capital, focuses on gap-closing rather than diversity for its own sake, funding startups that address structural inequalities in access, opportunity, or outcomes, regardless of founder demographics. He discusses the Humanity AI initiative and argues that philanthropy needs to develop AI literacy and technical capacity, with some foundations hiring chief technology officers to effectively engage with these issues. He believes targeted interventions can create meaningful change without matching the massive investments of the major AI labs. Kapor expresses hope that a younger generation of leaders in tech and philanthropy can step up to make positive differences, emphasizing that his generation should empower them rather than occupying seats at the table. Mitch Kapor is a pioneering technology entrepreneur, investor, and philanthropist who founded Lotus Development Corporation and created Lotus 1-2-3, the breakthrough spreadsheet software that helped establish the PC software industry in the 1980s. He co-founded the Electronic Frontier Foundation to advocate for digital rights and civil liberties, and later established Kapor Capital with his wife Freada Kapor Klein to invest in startups that close gaps of access, opportunity, and outcome for underrepresented communities. Kapor recently completed a masters degree at the MIT Sloan School focused on gap-closing investing, returning to finish what he started 45 years earlier when he left MIT to pursue his career in Silicon Valley. He serves on the steering committee of Humanity AI, a $500 million initiative to ensure AI benefits society broadly.

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• Brad Carson: Sharing AI's Bounty

  Former Congressman and Pentagon official Brad Carson discusses his organization, Americans for Responsible Innovation (ARI), which seeks to bridge the gap between immediate AI harms like and catastrophic safety risks, while bringing deep Capitol Hill expertise to the AI conversation . He argues that unlike previous innovations such as electricity or the automobile, AI has been deeply unpopular with the public from the start, creating a rare bipartisan alignment among those skeptical of its power and impacts. This creates openings for productive discussions about AI policy. Drawing on his military experience, Carson suggests that while AI will shorten the kill chain, it won't fundamentally change the human nature of warfare, and he warns against the US military's tendency to seek technical solutions to human problems . The conversation covers current policy debates, highlighting the necessity of regulating the design of models rather than just their deployment, and the importance of export controls to maintain the West's advantage in compute . Ultimately, Carson emphasizes that for AI to succeed politically, the "bounty" of this technology must be shared broadly to avoid tearing apart the social fabric Brad Carson is the founder and president of Americans for Responsible Innovation (ARI), an organization dedicated to lobbying for policy that ensures artificial intelligence benefits the public interest. A former Rhodes Scholar, Carson has had a diverse career in public service, having served as a U.S. Congressman from Oklahoma, the Undersecretary of the Army, and the acting Undersecretary of Defense for Personnel and Readiness . He also served as a university president and deployed to Iraq in 2008 . Transcript Former TU President Brad Carson Pushes for Strong AI Guardrails   

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• Oliver Patel: Sharing Frameworks for AI Governance

  Oliver Patel has built a sizeable online following for his social media posts and Substack about enterprise AI governance, using clever acronyms and visual frameworks to distill down insights based on his experience at AstraZeneca, a major global pharmaceutical company. In this episode, he details his career journey from academic theory to government policy and now practical application, and offers insights for those new to the field. He argues that effective enterprise AI governance requires being pragmatic and picking your battles, since the role isn't to stop AI adoption but to enable organizations to adopt it safely and responsibly at speed and scale. He notes that core pillars of modern AI governance, such as AI literacy, risk classification, and maintaining an AI inventory, are incorporated into the EU AI Act and thus essential for compliance. Looking forward, Patel identifies AI democratization—how to govern AI when everyone in the workforce can use and build it—as the biggest hurdle, and offers thougths about how enteprises can respond. Oliver Patel is the Head of Enterprise AI Governance at AstraZeneca. Before moving into the corporate sector, he worked for the UK government as Head of Inbound Data Flows, where he focused on data policy and international data transfers, and was a researcher at University College London. He serves as an IAPP Faculty Member and a member of the OECD's Expert Group on AI Risk. His forthcoming book, Fundamentals of AI Governance, will be released in early 2026. Transcript Enterprise AI Governance Substack Top 10 Challenges for AI Governance Leaders in 2025 (Part 1)  Fundamentals of AI Governance book page  

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• Ravit Dotan: Rethinking AI Ethics

  Ravit Dotan argues that the primary barrier to accountable AI is not a lack of ethical clarity, but organizational roadblocks. While companies often understand what they should do, the real challenge is organizational dynamics that prevent execution—AI ethics has been shunted into separate teams lacking power and resources, with incentive structures that discourage engineers from raising concerns. Drawing on work with organizational psychologists, she emphasizes that frameworks prescribe what systems companies should have but ignore how to navigate organizational realities. The key insight: responsible AI can't be a separate compliance exercise but must be embedded organically into how people work. Ravit discusses a recent shift in her orientation from focusing solely on governance frameworks to teaching people how to use AI thoughtfully. She critiques "take-out mode" where users passively order finished outputs, which undermines skills and critical review. The solution isn't just better governance, but teaching workers how to incorporate responsible AI practices into their actual workflows.  Dr. Ravit Dotan is the founder and CEO of TechBetter, an AI ethics consulting firm, and Director of the Collaborative AI Responsibility (CAIR) Lab at the University of Pittsburgh. She holds a Ph.D. in Philosophy from UC Berkeley and has been named one of the "100 Brilliant Women in AI Ethics" (2023), and was a finalist for "Responsible AI Leader of the Year" (2025). Since 2021, she has consulted with tech companies, investors, and local governments on responsible AI. Her recent work emphasizes teaching people to use AI thoughtfully while maintaining their agency and skills. Her work has been featured in The New York Times, CNBC, Financial Times, and TechCrunch. Transcript My New Path in AI Ethics (October 2025) The Values Encoded in Machine Learning Research (FAccT 2022 Distinguished Paper Award) - Responsible AI Maturity Framework  

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• Trey Causey: Is Responsble AI Failing?

  Kevin Werbach speaks with Trey Causey about the precarious state of the responsible AI (RAI) field. Causey argues that while the mission is critical, the current organizational structures for many RAI teams are struggling. He highlights a fundamental conflict between business objectives and governance intentions, compounded by the fact that RAI teams' successes (preventing harm) are often invisible, while their failures are highly visible. Causey makes the case that for RAI teams to be effective, they must possess deep technical competence to build solutions and gain credibility with engineering teams. He also explores the idea of "epistemic overreach," where RAI groups have been tasked with an impossibly broad mandate they lack the product-market fit to fulfill. Drawing on his experience in the highly regulated employment sector at Indeed, he details the rigorous, science-based approach his team took to defining and measuring bias, emphasizing the need to move beyond simple heuristics and partner with legal and product teams before analysis even begins. Trey Causey is a data scientist who most recently served as the Head of Responsible AI for Indeed. His background is in computational sociology, where he used natural language processing to answer social questions. Transcript Responsible Ai Is Dying. Long Live Responsible AI 

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