Aaron Roth
Associate Professor of Computer and Information Science
University of Pennsylvania

with commentary by:

Richard Berk
Chair, Department of Criminology
Professor of Statistics and Criminology
University of Pennsylvania

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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’s objective function was not designed with discriminatory intent.  Professor Berk will offer additional commentary.

The Optimizing Government Project is a Fels Policy Research Initiative and is open to the public. 

Panel discussion featuring:

Samuel Freeman
Avalon Professor of the Humanities, Department of Philosophy
University of Pennsylvania

Nancy Hirschmann
Professor of Political Science
University of Pennsylvania

Seth Kreimer 
Kenneth W. Gemmill Professor of Law
University of Pennsylvania

Moderated by:

Cary Coglianese 
Edward B. Shils Professor of Law and Professor of Political Science 
University of Pennsylvania

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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 “fairness” 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.  

The Optimizing Government Project is a Fels Policy Research Initiative and is open to the public. 

This program has been approved for 1.5 total CLE credits (1.0 substantive, 0.5 ethics) for Pennsylvania lawyers. CLE credit may be available in other jurisdictions as well. Attendees seeking CLE credit should bring separate payment in the amount of $60.00 ($30.00 public interest/non-profit attorneys) cash or check made payable to The Trustees of the University of Pennsylvania.

Michael Kearns
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

with commentary by: 

Sandra Mayson
Research Fellow
Quattrone Center for the Fair Administration of Justice
University of Pennsylvania

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Opaque machine learning models continue to be adopted by governments in a range of contexts, raising questions about whether such automated decisions are compatible with notions of fairness and equal protection. Answering these questions will require both careful policy study and a technical understanding of what makes algorithmic decision-making effective. This workshop will review technical solutions to these challenges and how they might impact government use of automated decision-making processes. The workshop is the third in a series taking place throughout the fall, dedicated to exploring policy and technical challenges to using artificial intelligence in government.  

The Optimizing Government Project is a Fels Policy Research Initiative and is open to the public.