Skip to content
Cynthia Rudin
Cynthia Rudin

Cynthia Rudin

Gilbert, Louis, and Edward Lehrman Distinguished Professor of Computer Science and Electrical and Computer Engineering
Duke University
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.


Many Good Models Leads To Amazing Things

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. Cynthia shows 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) algorithm choice, specifically, providing advanced knowledge of which algorithms might be suitable for a given problem, (4) public policy, and (5) scientific discovery. Cynthia will also discuss a theory of when the Rashomon Effect occurs and why. Attendees will see how the existence of many good models due to the Rashomon Effect can have a massive impact on the use of machine learning for complex problems in society. 

In this presentation, Cynthia will be discussing the paper “Amazing Things Come From Having Many Good Models” (ICML spotlight, 2024) which is joint work with Chudi Zhong, Lesia Semenova, Margo Seltzer, Ronald Parr, Jiachang Liu, Srikar Katta, Jon Donnelly, Harry Chen, and Zachery Boner. https://arxiv.org/abs/2407.04846

Essential / Professional / Leadership