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Colloquium

Many Good Models Leads To…

Speaker: Cynthia Rudin Professor in Computer Science and Electrical and Computer Engineering at Duke University Duke University Wednesday, January 14, 2026 11:30AM - 1:00PM Lunch at 11:30am in 1307 Talk 12:00-1:00pm in 1327 Location: Yale Institute for Foundations of Data Science & Webcast, 219 Prospect Street, New Haven, CT 06511 and via Webcast: https://yale.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=3f6a1582-aa2d-4e97-bb0b-b3ca013c14e5

Abstract: As it turns out, many good models leads to amazing things! The Rashomon Effect, coined by Leo Breiman, describes the phenomenon that there exist many equally good predictive models for the same dataset. This phenomenon happens for many real datasets and when it does, it sparks both magic and consternation, but mostly magic. In light of the Rashomon Effect, my collaborators and I propose to reshape the way we think about machine learning, particularly for tabular data problems in the nondeterministic (noisy) setting. I’ll address how the Rashomon Effect impacts (1) the existence of simple-yet-accurate models, (2) flexibility to address user preferences, such as fairness and monotonicity, without losing performance, (3) uncertainty in predictions, fairness, and explanations, (4) reliable variable importance, (5) algorithm choice, specifically, providing advanced knowledge of which algorithms might be suitable for a given problem, and (6) public policy. I’ll also discuss a theory of when the Rashomon Effect occurs and why: interestingly, noise in data leads to a large Rashomon Effect. My goal is to illustrate how the Rashomon Effect can have a massive impact on the use of machine learning for complex problems in society.

Speaker Bio: Cynthia Rudin is the Gilbert, Louis, and Edward Lehrman Distinguished Professor in Computer Science and Electrical and Computer Engineering at Duke University. She works in interpretable machine learning, and aims to design predictive models that people can understand. She is the recipient of the IJCAI-25 John McCarthy Award, the 2024 INFORMS Society on Data Mining Prize, the 2022 AAAI Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity, the INFORMS Innovative Applications in Analytics Award, and is a 2022 Guggenheim Fellow.

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