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.

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My New Path in AI Ethics (October 2025)

The Values Encoded in Machine Learning Research (FAccT 2022 Distinguished Paper Award) -

Responsible AI Maturity Framework

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.

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Responsible Ai Is Dying. Long Live Responsible AI

Kevin Werbach speaks with Caroline Louveaux, Chief Privacy, AI, and Data Responsibility Officer at Mastercard, about what it means to make trust mission critical in the age of artificial intelligence. Caroline shares how Mastercard built its AI governance program long before the current AI boom, grounding it in the company's Data and Technology Responsibility Principles". She explains how privacy-by-design practices evolved into a single global AI governance framework aligned with the EU AI Act, NIST AI Risk Management, and standards.

The conversation explores how Mastercard balances innovation speed with risk management, automates low-risk assessments, and maintains executive oversight through its AI Governance Council. Caroline also discusses the company's work on agentic commerce, where autonomous AI agents can initiate payments, and why trust, certification, and transparency are essential for such systems to succeed. Caroline unpacks what it takes for a global organization to innovate responsibly — from cross-functional governance and "tone from the top," to partnerships like the Data & Trust Alliance and efforts to harmonize global standards. Caroline emphasizes that responsible AI is a shared responsibility and that companies that can "innovate fast, at scale, but also do so responsibly" will be the ones that thrive.

Caroline Louveaux leads Mastercard's global privacy and data responsibility strategy. She has been instrumental in building Mastercard's AI governance framework and shaping global policy discussions on data and technology. She serves on the board of the International Association of Privacy Professionals (IAPP), the WEF Task Force on Data Intermediaries, the ENISA Working Group on AI Cybersecurity, and the IEEE AI Systems Risk and Impact Executive Committee, among other activities.

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How Mastercard Uses AI Strategically: A Case Study (Forbes 2024)

Lessons From a Pioneer: Mastercard's Experience of AI Governance (IMD, 2023)

As AI Agents Gain Autonomy, Trust Becomes the New Currency. Mastercard Wants to Power Both. (Business Insider, July 2025)