Philip J. Cook Jens Ludwig Adam M. Samaha
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Nber Working Paper Series Human Decisions And
NBER WORKING PAPER SERIES HUMAN DECISIONS AND MACHINE PREDICTIONS Jon Kleinberg Himabindu Lakkaraju Jure Leskovec Jens Ludwig Sendhil Mullainathan Working Paper 23180 http://www.nber.org/papers/w23180 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 February 2017 We are immensely grateful to Mike Riley for meticulously and tirelessly spearheading the data analytics, with effort well above and beyond the call of duty. Thanks to David Abrams, Matt Alsdorf, Molly Cohen, Alexander Crohn, Gretchen Ruth Cusick, Tim Dierks, John Donohue, Mark DuPont, Meg Egan, Elizabeth Glazer, Judge Joan Gottschall, Nathan Hess, Karen Kane, Leslie Kellam, Angela LaScala-Gruenewald, Charles Loeffler, Anne Milgram, Lauren Raphael, Chris Rohlfs, Dan Rosenbaum, Terry Salo, Andrei Shleifer, Aaron Sojourner, James Sowerby, Cass Sunstein, Michele Sviridoff, Emily Turner, and Judge John Wasilewski for valuable assistance and comments, to Binta Diop, Nathan Hess, and Robert Webberfor help with the data, to David Welgus and Rebecca Wei for outstanding work on the data analysis, to seminar participants at Berkeley, Carnegie Mellon, Harvard, Michigan, the National Bureau of Economic Research, New York University, Northwestern, Stanford and the University of Chicago for helpful comments, to the Simons Foundation for its support of Jon Kleinberg's research, to the Stanford Data Science Initiative for its support of Jure Leskovec’s research, to the Robert Bosch Stanford Graduate Fellowship for its support of Himabindu Lakkaraju and to Tom Dunn, Ira Handler, and the MacArthur, McCormick and Pritzker foundations for their support of the University of Chicago Crime Lab and Urban Labs. The main data we analyze are provided by the New York State Division of Criminal Justice Services (DCJS), and the Office of Court Administration. -
Himabindu Lakkaraju
Himabindu Lakkaraju Contact Information 428 Morgan Hall Harvard Business School Soldiers Field Road Boston, MA 02163 E-mail: [email protected] Webpage: http://web.stanford.edu/∼himalv Research Interests Transparency, Fairness, and Safety in Artificial Intelligence (AI); Applications of AI to Criminal Justice, Healthcare, Public Policy, and Education; AI for Decision-Making. Academic & Harvard University 11/2018 - Present Professional Postdoctoral Fellow with appointments in Business School and Experience Department of Computer Science Microsoft Research, Redmond 5/2017 - 6/2017 Visiting Researcher Microsoft Research, Redmond 6/2016 - 9/2016 Research Intern University of Chicago 6/2014 - 8/2014 Data Science for Social Good Fellow IBM Research - India, Bangalore 7/2010 - 7/2012 Technical Staff Member SAP Research, Bangalore 7/2009 - 3/2010 Visiting Researcher Adobe Systems Pvt. Ltd., Bangalore 7/2007 - 7/2008 Software Engineer Education Stanford University 9/2012 - 9/2018 Ph.D. in Computer Science Thesis: Enabling Machine Learning for High-Stakes Decision-Making Advisor: Prof. Jure Leskovec Thesis Committee: Prof. Emma Brunskill, Dr. Eric Horvitz, Prof. Jon Kleinberg, Prof. Percy Liang, Prof. Cynthia Rudin Stanford University 9/2012 - 9/2015 Master of Science (MS) in Computer Science Advisor: Prof. Jure Leskovec Indian Institute of Science (IISc) 8/2008 - 7/2010 Master of Engineering (MEng) in Computer Science & Automation Thesis: Exploring Topic Models for Understanding Sentiments Expressed in Customer Reviews Advisor: Prof. Chiranjib -
2018 Annual Report Alfred P
2018 Annual Report Alfred P. Sloan Foundation $ 2018 Annual Report Contents Preface II Mission Statement III From the President IV The Year in Discovery VI About the Grants Listing 1 2018 Grants by Program 2 2018 Financial Review 101 Audited Financial Statements and Schedules 103 Board of Trustees 133 Officers and Staff 134 Index of 2018 Grant Recipients 135 Cover: The Sloan Foundation Telescope at Apache Point Observatory, New Mexico as it appeared in May 1998, when it achieved first light as the primary instrument of the Sloan Digital Sky Survey. An early set of images is shown superimposed on the sky behind it. (CREDIT: DAN LONG, APACHE POINT OBSERVATORY) I Alfred P. Sloan Foundation $ 2018 Annual Report Preface The ALFRED P. SLOAN FOUNDATION administers a private fund for the benefit of the public. It accordingly recognizes the responsibility of making periodic reports to the public on the management of this fund. The Foundation therefore submits this public report for the year 2018. II Alfred P. Sloan Foundation $ 2018 Annual Report Mission Statement The ALFRED P. SLOAN FOUNDATION makes grants primarily to support original research and education related to science, technology, engineering, mathematics, and economics. The Foundation believes that these fields—and the scholars and practitioners who work in them—are chief drivers of the nation’s health and prosperity. The Foundation also believes that a reasoned, systematic understanding of the forces of nature and society, when applied inventively and wisely, can lead to a better world for all. III Alfred P. Sloan Foundation $ 2018 Annual Report From the President ADAM F. -
Human Decisions and Machine Predictions*
HUMAN DECISIONS AND MACHINE PREDICTIONS* Jon Kleinberg Himabindu Lakkaraju Jure Leskovec Jens Ludwig Sendhil Mullainathan August 11, 2017 Abstract Can machine learning improve human decision making? Bail decisions provide a good test case. Millions of times each year, judges make jail-or-release decisions that hinge on a prediction of what a defendant would do if released. The concreteness of the prediction task combined with the volume of data available makes this a promising machine-learning application. Yet comparing the algorithm to judges proves complicated. First, the available data are generated by prior judge decisions. We only observe crime outcomes for released defendants, not for those judges detained. This makes it hard to evaluate counterfactual decision rules based on algorithmic predictions. Second, judges may have a broader set of preferences than the variable the algorithm predicts; for instance, judges may care specifically about violent crimes or about racial inequities. We deal with these problems using different econometric strategies, such as quasi-random assignment of cases to judges. Even accounting for these concerns, our results suggest potentially large welfare gains: one policy simulation shows crime reductions up to 24.7% with no change in jailing rates, or jailing rate reductions up to 41.9% with no increase in crime rates. Moreover, all categories of crime, including violent crimes, show reductions; and these gains can be achieved while simultaneously reducing racial disparities. These results suggest that while machine learning can be valuable, realizing this value requires integrating these tools into an economic framework: being clear about the link between predictions and decisions; specifying the scope of payoff functions; and constructing unbiased decision counterfactuals. -
Curriculum Vitae February, 2021 JENS LUDWIG
Curriculum Vitae February, 2021 JENS LUDWIG OFFICE ADDRESS University of Chicago 1307 East 60th Street, Room 2065 Chicago, IL 60637 (773) 834-0811 [email protected] EDUCATION Ph.D. Economics, Duke University, Durham, NC 1994 B.A. Economics, (Minor in Religion), Rutgers College, New Brunswick, NJ, 1990 CURRENT ACADEMIC APPOINTMENTS 2019-present Edwin A. and Betty L. Bergman Distinguished Service Professor, University of Chicago 2019-present Pritzker Co-Director, University of Chicago Urban Education Lab 2019-present Pritzker Director, University of Chicago Crime Lab PREVIOUS ACADEMIC APPOINTMENTS 2011-2019 Co-Director, University of Chicago Urban Education Lab 2008-2019 Director, University of Chicago Crime Lab 2008-2018 McCormick Foundation Professor of Social Service Administration, Law, and Public Policy, University of Chicago 2011-2012 Academic Director for Students, Harris School for Public Policy Studies, University of Chicago 2007-2008 Professor of Social Service Administration, Law, and Public Policy, University of Chicago 2006-2007 Professor of Public Policy, Georgetown University 2006-2007 Associate Dean for Public Policy Admissions, Georgetown University 2001-2002 Andrew W. Mellon Fellow in Economic Studies, The Brookings Institution 2001-2006 Associate Professor of Public Policy, Georgetown University 1997-1998 Visiting Scholar, Northwestern / University of Chicago Joint Center for Poverty Research 1994-2001 Assistant Professor of Public Policy, Georgetown University OTHER AFFILIATIONS 2014-2019 Crime Program Chair, Abdul -
REDUCING GUN VIOLENCE in AMERICA Informing Policy with Evidence and Analysis
REDUCING GUN VIOLENCE IN AMERICA Informing Policy with Evidence and Analysis Edited by DANIEL W. WEBSTER and JON S. VERNICK Foreword by MICHAEL R. BLOOMBERG Reducing Gun Violence in America This page intentionally left blank Reducing Gun Violence in America Informing Policy with Evidence and Analysis edited by Daniel W. Webster, ScD, MPH, and Jon S. Vernick, JD, MPH Center for Gun Policy and Research Johns Hopkins Bloomberg School of Public Health The Johns Hopkins University Press Baltimore © 2013 The Johns Hopkins University Press All rights reserved. Published 2013 Printed in the United States of America on acid- free paper 9 8 7 6 5 4 3 2 1 The Johns Hopkins University Press 2715 North Charles Street Baltimore, Mary land 21218- 4363 w w w . p r e s s . j h u . e d u Library of Congress Control Number: 2013930408 A cata log record for this book is available from the British Library. ISBN 13: 978-1-4214-1110-1 (pbk. : alk. paper) ISBN 10: 1-4214-1110-5 (pbk. : alk. paper) ISBN 13: 978-1-4214-1111-8 (electronic) ISBN 10: 1-4214-1111-3 (electronic) Special discounts are available for bulk purchases of this book. For more information, please contact Special Sales at 410- 516- 6936 or [email protected]. The Johns Hopkins University Press uses environmentally friendly book materials, including recycled text paper that is composed of at least 30 percent post- consumer waste, whenever possible. To victims of gun violence and to those who work daily to reduce it This page intentionally left blank Contents Foreword xi Michael R. -
Race, Crime, and Incarceration Policy in the United States
Senior Seminar: Race, Crime, and Incarceration Policy in the United States BC3063 (Fall 2021) Instructor: Professor Morgan C. Williams, Jr. Email: [email protected] Office Hours: Wednesdays 2 PM – 4 PM (Appointment Only) Office Hours Scheduling: All office hours appointments can be made, at least 24 hours in advance, using the Calendly: https://calendly.com/mcwillia/officehoursfall2021 Course Information Class Meeting Times: Wednesday, 12:00 PM – 1:50 PM Class Location: TBD Notes: This syllabus is subject to change as we approach and move forward with the Fall 2021 semester. This course intends on meeting in-person throughout the Fall 2021 semester unless public health conditions or Barnard College suggests otherwise. I strongly suggest that all students review the College’s Health & Safety guidance well before the first day of classes. Course Description Recent momentum behind criminal justice reform permitted new discussions concerning incarceration policy and punishment in the United States. This course examines the role of crime, incarceration policy, and institutions in driving contemporary discussions on criminal justice reform through the prism of race. The course will provide students with an opportunity to critically examine how behaviors such as racial stereotypes and stigma influence a variety of issues at the center of criminal justice reform–including racial disparities in crime, law enforcement strategy, sentencing outcomes, and prisoner reentry into the labor market. Students will ultimately learn that even as a pure social construct, race plays a vital role in shaping both social policy and outcomes related to criminal justice reform. Prerequisites This course requires permission from the instructor in addition to the completion of all required coursework for the economics track, political economy track, or economics and mathematics majors. -
Machine Learning and Development Policy
Machine Learning and Development Policy Sendhil Mullainathan (joint papers with Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, Ziad Obermeyer) Magic? • Hard not to be wowed • But what makes it tick? • Could that be used elsewhere? In my own work? AI Approach • We do it perfectly. • Introspect how • Program that up Programming • For each review make a vector of words • Figure out whether it has positive words and negative words • Count Trying to Copy Humans Brilliant Suck Dazzling Cliched Cool 60% Slow Gripping Awful Moving Bad This Approach Stalled • “Trivial” problems proved impossible – Marvin Minsky once assigned "the problem of computer vision" as a summer project • Forget about the more “complicated” problems like language What is the magic trick? • Make this an empirical exercise – Collect some data • Look at what combination of words predicts being a good review • Example dataset: – 2000 movie reviews – 1000 good and 1000 bad reviews Learning not Programming STILL Bad Love Stupid Superb 95% Worst Great ? ! Pang, Lee and Vaithyanathan Machine learning • Turn any “intelligence” task into an empirical learning task – Specify what is to be predicted – Specify what is used to predict it Y = 0, 1 { } Positive? X = 0, 1 k |{{z }} Word Vector ˆ f = argmin| {z }f E[L(f(x),y)] Machine learning • Turn any “intelligence” task into an empirical learning task – Specify what is to be predicted – Specify what is used to predict it • Underneath most machine intelligence you see • Not a coincidence that ML and big data arose together -
Productivity and Selection of Human Capital with Machine Learning
Productivity and Selection of Human Capital with Machine Learning By AARON CHALFIN, OREN DANIELI, ANDREW HILLIS, ZUBIN JELVEH, MICHAEL LUCA, JENS LUDWIG, AND SENDHIL MULLAINATHAN* * Chalfin: University of Chicago, Chicago IL 60637 (email: a great deal. Variability in worker productivity [email protected]); Danieli: Harvard University, Cambridge MA 02138 (email: [email protected]); Hillis: Harvard (e.g., Gordon, Kane and Staiger, 2006) means University, Cambridge MA 02138 (email: [email protected]); ∂Y/∂L depends on which new teacher or cop Jelveh: New York University, New York NY 10003 ([email protected]); Luca: Harvard Business School, Boston, is hired. Heterogeneity in productivity also MA 02163 (email: [email protected]); Ludwig: University of Chicago, means that estimates for the effect of hiring Chicago IL 60637 and NBER (email: [email protected]); Mullainathan: Harvard University, Cambridge MA 02138 and NBER one more worker are not stable across contexts (email: [email protected]). Paper prepared for the American – they depend on the institutions used to Economic Association session “Predictive Cities,” January 5, 2016. This paper uses data obtained from the University of Michigan’s screen and hire the marginal worker. Inter-university Consortium on Political and Social Research. Thanks With heterogeneity in labor inputs, to the National Science Foundation Graduate Research Fellowship Program (Grant No. DGE1144152), and to the Laura and John Arnold economics can offer two contributions to the Foundation, the John D. and Catherine T. MacArthur Foundation, the study of productivity in social policy. The first Robert R. McCormick Foundation, the Pritzker Foundation, and to Susan and Tom Dunn and Ira Handler for support of the University of is standard causal inference around shifts in Chicago Crime Lab. -
Download Course Syllabus
AEA Continuing Education Workshop San Francisco, CA 2016 Syllabus draft date: August 31, 2015 The Economics of Crime John J. Donohue III Stanford University & NBER Jens Ludwig University of Chicago & NBER Overview This course will provide an overview of core issues and recent advances in the economics of crime. The class will cover standard law and economics topics related to the criminal justice system, including incarceration, policing, the deterrent effect of the death penalty, gun policy, drug policy, and racial bias within the criminal justice system. The class will also consider the role that social factors play in affecting crime and violence, such as abortion policies, social programs and other social conditions such as education, poverty, family structure and even environmental factors (such as lead exposure). Lectures will also emphasize methodological advances in the economic study of crime, including issues related to identification, meta- analysis, randomized controlled trials, accounting for multiple comparisons for statistical inference, synthetic controls, behavioral economics and crime, and machine learning. Instructors John J. Donohue III is the C. Wendell and Edith M. Carlsmith Professor of Law at the Stanford Law School. He is one of the leading empirical researchers on the economics of crime over the past 25 years, whose work includes studies of the death penalty, drug policy, gun control, and the effects of abortion legalization on crime. He is the former editor of the American Law and Economics Review, president of the American Law and Economics Association and an elected member of the American Academy of Arts and Sciences. He earned his PhD in economics at Yale and his JD from Harvard Law School. -
Himabindu Lakkaraju
Himabindu Lakkaraju Contact Information 428 Morgan Hall Harvard Business School Soldiers Field Road Boston, MA 02163 E-mail: [email protected]; [email protected] Webpage: http://himalakkaraju.github.io Research Interests Transparency, Fairness, and Safety in Artificial Intelligence (AI); Applications of AI to Law, Healthcare, Public Policy, and Education; AI for Decision-Making. Academic & Harvard University 01/2020 - Professional Assistant Professor with appointments in Business School and Experience Department of Computer Science Harvard University 11/2018 - 12/2019 Postdoctoral Fellow at Harvard Business School Stanford University 9/2012 - 9/2018 Research Assistant in the Department of Computer Science Microsoft Research, Redmond 5/2017 - 6/2017 Visiting Researcher Microsoft Research, Redmond 6/2016 - 9/2016 Research Intern University of Chicago 6/2014 - 8/2014 Data Science for Social Good Fellow IBM Research - India, Bangalore 7/2010 - 7/2012 Technical Staff Member SAP Research, Bangalore 7/2009 - 3/2010 Visiting Researcher Adobe Systems Pvt. Ltd., Bangalore 7/2007 - 7/2008 Software Engineer Education Stanford University 9/2012 - 9/2018 Ph.D. in Computer Science Thesis: Enabling Machine Learning for High-Stakes Decision-Making Stanford University 9/2012 - 9/2015 Master of Science (MS) in Computer Science Indian Institute of Science (IISc) 8/2008 - 7/2010 Master of Engineering (MEng) in Computer Science & Automation Thesis: Exploring Topic Models for Understanding Sentiments Expressed in Customer Reviews Selected Honors & -
Nber Working Paper Series Human Decisions And
NBER WORKING PAPER SERIES HUMAN DECISIONS AND MACHINE PREDICTIONS Jon Kleinberg Himabindu Lakkaraju Jure Leskovec Jens Ludwig Sendhil Mullainathan Working Paper 23180 http://www.nber.org/papers/w23180 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 February 2017 We are immensely grateful to Mike Riley for meticulously and tirelessly spearheading the data analytics, with effort well above and beyond the call of duty. Thanks to David Abrams, Matt Alsdorf, Molly Cohen, Alexander Crohn, Gretchen Ruth Cusick, Tim Dierks, John Donohue, Mark DuPont, Meg Egan, Elizabeth Glazer, Judge Joan Gottschall, Nathan Hess, Karen Kane, Leslie Kellam, Angela LaScala-Gruenewald, Charles Loeffler, Anne Milgram, Lauren Raphael, Chris Rohlfs, Dan Rosenbaum, Terry Salo, Andrei Shleifer, Aaron Sojourner, James Sowerby, Cass Sunstein, Michele Sviridoff, Emily Turner, and Judge John Wasilewski for valuable assistance and comments, to Binta Diop, Nathan Hess, and Robert Webberfor help with the data, to David Welgus and Rebecca Wei for outstanding work on the data analysis, to seminar participants at Berkeley, Carnegie Mellon, Harvard, Michigan, the National Bureau of Economic Research, New York University, Northwestern, Stanford and the University of Chicago for helpful comments, to the Simons Foundation for its support of Jon Kleinberg's research, to the Stanford Data Science Initiative for its support of Jure Leskovec’s research, to the Robert Bosch Stanford Graduate Fellowship for its support of Himabindu Lakkaraju and to Susan and Tom Dunn, Ira Handler, and the MacArthur, McCormick and Pritzker foundations for their support of the University of Chicago Crime Lab and Urban Labs. The main data we analyze are provided by the New York State Division of Criminal Justice Services (DCJS), and the Office of Court Administration.