The Women in AI speaker series, a collaboration between the Schwartz Reisman Institute for Technology and Society and Deloitte, welcomes Cynthia Rudin, the Earl D. McLean, Jr. Professor of Computer Science, Electrical and Computer Engineering, Statistical Science, Mathematics, and Biostatistics & Bioinformatics at Duke University, where she directs the Interpretable Machine Learning Lab. Rudin is the recipient of the 2022 Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity from the Association for the Advancement of Artificial Intelligence (AAAI), a three-time winner of the INFORMS Innovative Applications in Analytics Award, and is a 2022 Guggenheim Fellow, as well as a fellow of the American Statistical Association, the Institute of Mathematical Statistics, and AAAI.
In this talk, Rudin will present her recent research on developing simpler models for machine learning that are able to provide the same level of accuracy as more complex, “black box” models, and new methods of optimization for discovering such models.
Talk title:
“Do simpler machine learning models exist and how can we find them?”
Abstract:
While the trend in machine learning has tended towards building more complicated “black box” models, such models are not as useful for high stakes decisions—black box models have led to mistakes in bail and parole decisions in criminal justice, flawed models in healthcare, and inexplicable loan decisions in finance. Simpler, interpretable models would be better. Thus, we consider questions that diametrically oppose the trend in the field: for which types of datasets would we expect to get simpler models at the same level of accuracy as black box models? If such simpler-yet-accurate models exist, how can we use optimization to find these simpler models? In this talk, I present an easy calculation to check for the possibility of a simpler (yet accurate) model before computing one. This calculation indicates that simpler-but-accurate models do exist in practice more often than you might think. Also, some types of these simple models are (surprisingly) small enough that they can be memorized or printed on an index card.
About Cynthia Rudin
Cynthia Rudin is the Earl D. McLean, Jr. Professor of Computer Science, Electrical and Computer Engineering, Statistical Science, Mathematics, and Biostatistics & Bioinformatics at Duke University, and directs the Interpretable Machine Learning Lab. Previously, 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 the recipient of the 2022 Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity from the Association for the Advancement of Artificial Intelligence (AAAI), the most prestigious award in the field of artificial intelligence. Rudin is also 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 Business Insider as one of the 12 most impressive professors at MIT in 2015. She is a 2022 Guggenheim Fellow, as well as a fellow of the American Statistical Association, the Institute of Mathematical Statistics, and AAAI.
Rudin 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, INFORMS, Machine Learning in Healthcare (MLHC), Fairness, Accountability and Transparency in Machine Learning (FAT-ML), ECML-PKDD, and the Nobel Conference. Her work has been featured in news outlets including the New York Times, Washington Post, Wall Street Journal, and Boston Globe.
About Women in AI
Women in AI is a six-part virtual speaker and mentorship series developed by the Schwartz Reisman Institute for Technology and Society in collaboration with Deloitte that connects a global audience with a diverse group of female thought leaders in the field of AI research. The Women in AI series convenes leading female AI researchers to share knowledge and mentorship opportunities through seminar events that promote opportunities for women across the technology sector. Participants will explore how women are leading the development of new technologies and approaches, and investigating emerging trends across the sector. The series provides insights into cutting-edge research, bold points of views, and help business and community leaders elevate diverse voices while promoting opportunities for women to share their perspectives.