CFEM Seminar - Title:"Fairness in Machine Learning" | Michael Kearns | University of Pennsylvania
Algorithms, including those that learn predictive models from historical data, are making increasingly consequential decisions about the lives of individual citizens in domains as diverse as advertising, credit, employment, education, and criminal sentencing. This trend has been accompanied by increasing concern and alarm over potential harms to social norms such as privacy, fairness, transparency and accountability. Recent research in machine learning seeks to quantify the extent that such social norms can actually be embedded algorithmically, and the trade-offs presented with predictive accuracy and other measures of utility. I will survey some of these developments, with a particular focus on what it might mean for machine learning to be fair.
Dr. Michael Kearns is Professor and National Center Chair in the Department of Computer and Information Sciences at the University of Pennsylvania, with secondary appointments in the departments of Economics, and in Statistics, and Operations, Information and Decisions of the Wharton School. Kearns is the Founding Director of Penn’s Warren Center for Network and Data Sciences, as well as the Penn Program in Networked and Social Systems Engineering. His research includes topics in machine learning, algorithmic game theory, computational social science, and quantitative finance and algorithmic trading. Kearns spent a decade at AT&T Bell Labs, where he was head of the AI department, which conducted a range of systems and foundational AI and machine learning research. He has consulted extensively in the technology and finance industries, and is currently Chief Scientist of MANA Partners, a New York trading and technology company. He studied math and computer science at UC Berkeley, and completed his Ph.D. in computer science at Harvard University in 1989.