Concerns about machine learning and artificial intelligence include the potential for discrimination, leaks of personal data, and inscrutable decisions made by unintelligible models.  Amidst recent calls for stricter regulation, there has been an explosion of research on how to forge better-behaved and more socially aware algorithm design. Michael Kearns, computer scientist and author, surveys these developments and attempts to place them in a broader societal context, exploring how we should and will make decisions in the future.

Cosponsored by the Department of Computer and Information Science and the Cinema & Media Studies Program.

Michael Kearns is a professor in the Computer and Information Science department at the University of Pennsylvania, where he holds the National Center Chair and has a joint appointment in Economics and the Wharton School. He is the founder of Penn’s Networked and Social Systems Engineering (NETS) program, and director of Penn’s Warren Center for Network and Data Sciences. His research interests include topics in machine learning, algorithmic game theory, social networks, and computational finance. He is a fellow of the American Academy of Arts and Sciences, the Association for Computing Machinery, and the Association for the Advancement of Artificial Intelligence. Kearns has consulted widely in the finance and technology industries, including a current role as an Amazon Scholar.