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 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.

Dr. Dietterich (AB Oberlin College 1977; MS University of Illinois 1979; PhD 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.