Abstract
With the widespread use of machine learning, there have been serious societal consequences from using black-box models for high-stakes decisions, including flawed bail and parole decisions in criminal justice. Explanations for black-box models are not reliable and can be misleading. If we use interpretable machine learning models, they come with their own explanations, which are faithful to what the model actually computes. I will give several reasons why we should use interpretable models, the most compelling of which is that for high stakes decisions, interpretable models do not seem to lose accuracy over black boxes - in fact, the opposite is true, where when we understand what the models are doing, we can troubleshoot them to ultimately gain accuracy.
Bio
Cynthia Rudin is a Professor of Computer Science, Electrical and Computer Engineering, Statistical Science, and Bio statistics & Bio informatics at Duke University, and directs the Interpretable Machine Learning Lab (formerly the Prediction Analysis Lab). Previously, Prof. Rudin held positions at MIT, Columbia, and NYU. She holds an undergraduate degree from the University at Buffalo and a PhD from Princeton University. She is a three-time winner of the INFORMS Innovative Applications in Analytics Award, was named as one of the “Top 40 Under 40” by Poets and Quants in 2015 and was named by Businessinsider.com as one of the 12 most impressive professors at MIT in 2015. She is the recipient of 2022 AAAI Squirrel AI award for pioneering socially responsible AI. She is a fellow of the American Statistical Association and a fellow of the Institute of Mathematical Statistics.
She is past chair of both the INFORMS Data Mining Section and the Statistical Learning and Data Science Section of the American Statistical Association. She has also served on committees for DARPA, the National Institute of Justice, AAAI, and ACM SIGKDD. She has served on three committees for the National Academies of Sciences, Engineering and Medicine, including the Committee on Applied and Theoretical Statistics, the Committee on Law and Justice, and the Committee on Analytic Research Foundations for the Next-Generation Electric Grid. She has given keynote/invited talks at several conferences including KDD (twice), AISTATS, CODE, Machine Learning in Healthcare (MLHC), Fairness, Accountability and Transparency in Machine Learning (FAT-ML), ECML-PKDD, and the Nobel Conference.