Algorithms have made our lives more efficient and entertaining-but not without a significant cost. Can we design a better future, one in which societal gains brought about by technology are balanced with the rights of citizens?
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<br><strong>Michael Kearns</strong> is Professor and the National Center Chair in the Computer and Information Science department of the University of Pennsylvania, where he has secondary appointments in Economics and the Wharton School. He is also the Founding Director of Penn's Warren Center for Network and Data<br>Sciences. Kearns has published widely in machine learning, artificial intelligence, algorithmic game theory and quantitative finance. He has worked extensively in the finance and technology industries, and consulted on various legal and regulatory matters involving algorithms, data, and machine<br>learning. Together with U.V. Vazirani, he is the author of <em>An Introduction to Computational Learning Theory.</em><br><br><strong>Aaron Roth </strong>is the class of 1940 Bicentennial Term Associate Professor in the Computer and Information Science department at the University of Pennsylvania, where he co-directs Penn's program in Networked and Social Systems Engineering. Roth has published widely in algorithms, machine learning, data<br>privacy, and algorithmic game theory, and has consulted extensively about algorithmic privacy. He is the recipient of numerous awards, including a Presidential Early Career Award for Scientists and Engineers (PECASE) awarded by President Obama in 2016. Together with Cynthia Dwork, he is the author<br>of <em>The Algorithmic Foundations of Differential Privacy.</em><br>
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