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Explore a remote talk by Sandra Zilles from the University of Regina that delves into formal models of machine teaching without collusion. This 55-minute presentation, part of the Theoretical Aspects of Trustworthy AI series at the Simons Institute, examines mathematical frameworks for teaching machines while preventing collusive behaviors that could compromise learning integrity. Learn about theoretical approaches that ensure trustworthy AI systems through proper teaching methodologies that maintain independence between teaching and learning processes.
