Thursday, September 22, 2016, 4:30pm\u00a0–\u00a06:00pm Aaron Roth<\/strong> View event recording and presentation materials<\/a><\/strong><\/p>\n\n\n\n Machine learning undergirds many technological advances today, from email spam filters to self-driving cars. It is also increasingly being used to make consequential decisions in domains such as lending, policing, and criminal sentencing. When machine learning moves from the private to the public sectors, and government uses algorithms to make decisions, additional concerns emerge, especially if machine learning produces inequitable outcomes. In this seminar, Professor Roth will offer a basic tutorial on machine learning, while also pointing out some of the basic pitfalls that can lead to discrimination even when an algorithm\u2019s objective function was not designed with discriminatory intent. Professor Berk will offer additional commentary.<\/p>\n\n\n\n Thursday, October 6, 2016, 4:30pm\u00a0–\u00a06:00pm Panel discussion featuring:<\/em><\/strong><\/p>\n\n\n\n Samuel Freeman<\/strong> Nancy Hirschmann<\/strong> Seth Kreimer <\/strong> Moderated by:<\/em><\/p>\n\n\n\n Cary Coglianese <\/strong> View event recording and presentation materials<\/a><\/strong><\/p>\n\n\n\n As machine learning is increasingly contemplated to support decision-making by governmental officials, questions have arisen about the fairness of decisions generated with the aid of artificial intelligence. Technologists seek to understand whether they can design algorithms to address fairness and equality concerns, while policymakers and citizens seek to evaluate available technological applications against standard moral and policy principles. Both technical and policy deliberation demands clarity about several key questions. What does \u201cfairness\u201d really mean? Does a commitment to equality demand merely that algorithms do not rely on characteristics such as race and gender to generate forecasts? Or does equality demand more? This workshop will bring together leading scholars from law, philosophy, and political theory to illuminate how fairness and equality are conceptualized in each of these fields. The workshop, the second in a series of four taking place throughout the fall, seeks to inform current deliberations about the design and use of machine learning in government.<\/p>\n\n\n\n Thursday, November 3, 2016, 4:30pm\u00a0–\u00a06:00pm Michael Kearns<\/strong> with commentary by:<\/em> <\/em><\/p>\n\n\n\n Sandra Mayson<\/strong>
<\/a>What is Machine Learning (and Why Might it be Unfair?)<\/a><\/p>\n\n\n\n
Associate Professor of Computer and Information Science
University of Pennsylvania
with commentary by:<\/em>
Richard Berk<\/strong>
Chair, Department of Criminology
Professor of Statistics and Criminology
University of Pennsylvania<\/p>\n\n\n\n
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<\/a>What is Fair and Equal Treatment?<\/a><\/p>\n\n\n\n
Avalon Professor of the Humanities, Department of Philosophy
University of Pennsylvania<\/p>\n\n\n\n
Professor of Political Science
University of Pennsylvania<\/p>\n\n\n\n
Kenneth W. Gemmill Professor of Law
University of Pennsylvania<\/p>\n\n\n\n
Edward B. Shils Professor of Law and Professor of Political Science
University of Pennsylvania<\/p>\n\n\n\n
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<\/a>Fairness and Performance Trade-Offs in Machine Learning<\/a><\/p>\n\n\n\n
Professor and National Center Chair
Department of Computer and Information Science
Founding Director, Warren Center for Network and Data Sciences
Founding Director, Penn Program in Networked and Social Systems Engineering
University of Pennsylvania<\/p>\n\n\n\n
Research Fellow
Quattrone Center for the Fair Administration of Justice
University of Pennsylvania<\/p>\n\n\n\n