On Rashomon Sets, the Mathematics of Simplicity, and Why We Don’t Need Black Boxes: An Interview with Cynthia Rudin

Summary

In this episode of AI Pioneers, Cynthia Rudin, a pioneer in interpretable machine learning and winner of the 2022 Squirrel AI Award, discusses her work on black boxes and Rashomon sets. She recounts her early project with Con Edison on the NYC power grid, where black-box models failed and simple interpretable models outperformed them. Rudin argues that for high-stakes problems like loan decisions or crime prediction, noisy data cannot support complex models, so black boxes are not necessarily more accurate. She explains her efforts to prove this mathematically and mentions applications from cancer detection to interpretable AI-generated music.