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.

My New Path in AI Ethics (October 2025)

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

Responsible AI Maturity Framework