"Steps Toward Trustworthy Machine Learning" - Tom Dietterich, Oregon State University

11:00 am - 12:00 pm

This event is part of the AI for Social Impact Seminar Series hosted by Penn State's Center for Socially Responsible Artificial Intelligence.

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“Steps Toward Trustworthy Machine Learning”

How can we trust systems built from machine learning components? We need advances in many areas, including machine learning algorithms, software engineering, ML ops, and explanation. This talk will describe our recent work in two important directions: obtaining calibrated performance estimates and performing run-time monitoring with guarantees. I will first describe recent work with Kiri Wagstaff on region-based calibration for classifiers and work with Jesse Hostetler on performance guarantees for reinforcement learning. Then, I'll review our research on providing guarantees for open category detection and anomaly detection for run-time monitoring of deployed systems. I'll conclude with some speculations concerning meta-cognitive situational awareness for AI systems.

About the Speaker

Dr. Dietterich (A.B. Oberlin College 1977; M.S. University of Illinois 1979; Ph.D. Stanford University 1984) is Distinguished Professor Emeritus in the School of Electrical Engineering and Computer Science at Oregon State University. Dietterich is one of the pioneers of the field of machine learning and has authored more than 200 refereed publications and two books. His current research topics include robust artificial intelligence, robust human-AI systems, and applications in sustainability. Dietterich has devoted many years of service to the research community. He is a former president of the Association for the Advancement of Artificial Intelligence and the founding president of the International Machine Learning Society. Other major roles include executive editor of the journal Machine Learning, co-founder of the Journal for Machine Learning Research, and program chair of AAAI 1990 and NIPS 2000. He currently serves as one of the moderators for the cs.LG category on arXiv.