Big Data and Bad Data: On the Sensitivity of Security Policy to Imperfect Information
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In this Essay, we examine some of the factors that make developing a “science of security” a significant research and policy challenge. We focus on how the empirical hurdles of missing data, inaccurate data, and invalid inferences can significantly impact—and sometimes impair—the security decisionmaking processes of individuals, firms, and policymakers. We offer practical examples of the sensitivity of policy modeling to those hurdles and highlight the relevance of these examples in the context of national security.
We thank Bruce Ackerman, Lucian Bebchuk, Robert Ellickson, Daniel Epps, Edward Fox, Jens Frankenreiter, Clayton Gillette, Brian Highsmith, Noah Kazis, Reinier Kraakman, Zachary Liscow, Jon Michaels, Mariana Pargendler, and David Schleicher, as well as those who provided feedback from presentations at Yale Law School and the annual meeting of the American Law and Economics Association. We also thank Josh Kaufman, Daniella Apodaca, Jonah Klausner, and the other editors of the University of Chicago Law Review for their excellent feedback on both substance and style.
We thank Bruce Ackerman, Lucian Bebchuk, Robert Ellickson, Daniel Epps, Edward Fox, Jens Frankenreiter, Clayton Gillette, Brian Highsmith, Noah Kazis, Reinier Kraakman, Zachary Liscow, Jon Michaels, Mariana Pargendler, and David Schleicher, as well as those who provided feedback from presentations at Yale Law School and the annual meeting of the American Law and Economics Association. We also thank Josh Kaufman, Daniella Apodaca, Jonah Klausner, and the other editors of the University of Chicago Law Review for their excellent feedback on both substance and style.
When one thinks of government, what comes to mind are familiar general-purpose entities like states, counties, cities, and townships. But more than half of the 90,000 governments in the United States are strikingly different: They are “special-purpose” governments that do one thing, such as supply water, fight fire, or pick up the trash. These entities remain understudied, and they present at least two puzzles. First, special-purpose governments are difficult to distinguish from entities that are typically regarded as business organizations—such as consumer cooperatives—and thus underscore the nebulous border between “public” and “private” enterprise. Where does that border lie? Second, special-purpose governments typically provide only one service, in sharp contrast to general-purpose governments. There is little in between the two poles—such as two-, three-, or four-purpose governments. Why? This Article answers those questions—and, in so doing, offers a new framework for thinking about special-purpose government.
We thank Lucian Bebchuk, Alon Brav, Ryan Bubb, Ed Cheng, Quinn Curtis, Elisabeth de Fontenay, Jared Ellias, Jill Fisch, Joe Grundfest, Cam Harvey, Scott Hirst, Colleen Honigsberg, Marcel Kahan, Louis Kaplow, Jonathan Klick, Brian Leiter, Saul Levmore, Dorothy Lund, John Morley, Mariana Pargendler, Elizabeth Pollman, Roberta Romano, Paolo Saguato, Holger Spamann, George Vojta, and Michael Weber for valuable suggestions and discussions. This Article has benefited from comments by workshop participants at Columbia Law School, George Mason University Antonin Scalia Law School, Georgetown University Law Center, Harvard Law School, Stanford Law School, UC Berkeley School of Law, the University of Chicago Law School, the University of Oxford Faculty of Law, the University of Pennsylvania Carey Law School, the University of Toronto Faculty of Law, the University of Virginia School of Law, and the Washington University School of Law, as well as at the American Law and Economics Association Annual Meeting, the Corporate & Securities Litigation Workshop, the Labex ReFi-NYU-SAFE/LawFin Law & Banking/Finance Conference, and the Utah Winter Deals Conference. Robertson gratefully acknowledges the support of the Douglas Clark and Ruth Ann McNeese Faculty Research Fund. Katy Beeson and Levi Haas provided exceptional research assistance. All errors are our own.
We thank Lucian Bebchuk, Alon Brav, Ryan Bubb, Ed Cheng, Quinn Curtis, Elisabeth de Fontenay, Jared Ellias, Jill Fisch, Joe Grundfest, Cam Harvey, Scott Hirst, Colleen Honigsberg, Marcel Kahan, Louis Kaplow, Jonathan Klick, Brian Leiter, Saul Levmore, Dorothy Lund, John Morley, Mariana Pargendler, Elizabeth Pollman, Roberta Romano, Paolo Saguato, Holger Spamann, George Vojta, and Michael Weber for valuable suggestions and discussions. This Article has benefited from comments by workshop participants at Columbia Law School, George Mason University Antonin Scalia Law School, Georgetown University Law Center, Harvard Law School, Stanford Law School, UC Berkeley School of Law, the University of Chicago Law School, the University of Oxford Faculty of Law, the University of Pennsylvania Carey Law School, the University of Toronto Faculty of Law, the University of Virginia School of Law, and the Washington University School of Law, as well as at the American Law and Economics Association Annual Meeting, the Corporate & Securities Litigation Workshop, the Labex ReFi-NYU-SAFE/LawFin Law & Banking/Finance Conference, and the Utah Winter Deals Conference. Robertson gratefully acknowledges the support of the Douglas Clark and Ruth Ann McNeese Faculty Research Fund. Katy Beeson and Levi Haas provided exceptional research assistance. All errors are our own.
We thank Lucian Bebchuk, Alon Brav, Ryan Bubb, Ed Cheng, Quinn Curtis, Elisabeth de Fontenay, Jared Ellias, Jill Fisch, Joe Grundfest, Cam Harvey, Scott Hirst, Colleen Honigsberg, Marcel Kahan, Louis Kaplow, Jonathan Klick, Brian Leiter, Saul Levmore, Dorothy Lund, John Morley, Mariana Pargendler, Elizabeth Pollman, Roberta Romano, Paolo Saguato, Holger Spamann, George Vojta, and Michael Weber for valuable suggestions and discussions. This Article has benefited from comments by workshop participants at Columbia Law School, George Mason University Antonin Scalia Law School, Georgetown University Law Center, Harvard Law School, Stanford Law School, UC Berkeley School of Law, the University of Chicago Law School, the University of Oxford Faculty of Law, the University of Pennsylvania Carey Law School, the University of Toronto Faculty of Law, the University of Virginia School of Law, and the Washington University School of Law, as well as at the American Law and Economics Association Annual Meeting, the Corporate & Securities Litigation Workshop, the Labex ReFi-NYU-SAFE/LawFin Law & Banking/Finance Conference, and the Utah Winter Deals Conference. Robertson gratefully acknowledges the support of the Douglas Clark and Ruth Ann McNeese Faculty Research Fund. Katy Beeson and Levi Haas provided exceptional research assistance. All errors are our own.
For years, academic experts have championed the widespread adoption of the “Fama-French” factors in legal settings. Factor models are commonly used to perform valuations, performance evaluation and event studies across a wide variety of contexts, many of which rely on data provided by Professor Kenneth French. Yet these data are beset by a problem that the experts themselves did not understand: In a companion article, we document widespread retroactive changes to French’s factor data. These changes are the result of discretionary changes to the construction of the factors and materially affect a broad range of estimates. In this Article, we show how these retroactive changes can have enormous impacts in precisely the settings in which experts have pressed for their use. We provide examples of valuations, performance analysis, and event studies in which the retroactive changes have a large—and even dispositive—effect on an expert’s conclusions.
She graduated from Tel-Aviv University and Harvard Law School. Named as one of the most cited legal scholars in the United States, and specifically the most cited scholar in employment law and one of the most cited in law and technology, she is influential in her field. Professor Lobel has served on President Obama’s policy team on innovation and labor market competition, has advised the Federal Trade Commission (FTC), and has published multiple books to critical acclaim. Her latest book, The Equality Machine, is an Economist Best Book of the Year.
This Essay argues for the development of more robust—and balanced—law that focuses not only on the risks, but also the potential, that AI brings. In turn, it argues that there is a need to develop a framework for laws and policies that incentivize and, at times, mandate transitions to AI-based automation. Automation rights—the right to demand and the duty to deploy AI-based technology when it outperforms human-based action—should become part of the legal landscape. A rational analysis of the costs and benefits of AI deployment would suggest that certain high-stakes circumstances compel automation because of the high costs and risks of not adopting the best available technologies. Inevitably, the rapid advancements in machine learning will mean that law soon must embrace AI; accelerate deployment; and, under certain circumstances, prohibit human intervention as a matter of fairness, welfare, and justice.