86.2
April
2019

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Essay
86.2
Down by Algorithms? Siphoning Rents, Exploiting Biases, and Shaping Preferences: Regulating the Dark Side of Personalized Transactions
Gerhard Wagner
Chair for Private Law, Business Law, and Law and Economics at the Humboldt University in Berlin and Academic Director of Humboldt’s LLM program on International Dispute Resolution
Horst Eidenmüller
Freshfields Professor of Commercial Law at the University of Oxford and Professorial Fellow of St. Hugh’s College, Oxford.

The rise of big data and artificial intelligence creates novel and unique opportunities for business to consumer (B2C) transactions. Businesses assemble or otherwise gain access to comprehensive sets of data on consumer preferences, behavior, and resources.

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86.2
Privatizing Personalized Law
Andrew Verstein
Associate Professor of Law, Wake Forest University School of Law. JD, Yale Law School.

I am grateful to all participants in The University of Chicago Law Review Symposium on Personalized Law for comments, but especially to Omri Ben-Shahar for insightful conversations.

In recent years, scholars have devoted increasing attention to the prospect of personalized law. The bulk of the literature has so far concerned whether to personalize any law and, if so, what substantive changes should be instantiated through personalization.

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86.2
Toward the Personalization of Copyright Law
Adi Libson
Assistant Professor, Bar-Ilan University School of Law
Gideon Parchomovsky
Robert G. Fuller Jr Professor of Law, University of Pennsylvania Law School
The dominant justification for copyright protection is that it is necessary to remedy an underproduction problem that arises from the public-good nature of expressive works.
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86.2
Assessing the Empirical Upside of Personalized Criminal Procedure
Matthew B. Kugler
Assistant Professor, Northwestern University Pritzker School of Law.

The authors thank Kiel Brennan-Marquez, Lee Fennell, Woodrow Hartzog, William Hubbard, Aziz Huq, Orin Kerr, Richard McAdams, Michael Pollack, John Rappaport, RichardRe, Victoria Schwartz, Christopher Slobogin, Rebecca Stone, and Alexander Stremitzer, along with workshop participants at UCLA Law School and The University of Chicago Law School, and attendees at the Privacy Law Scholars Conference, and The University of Chicago Law Review Symposium on Personalized Law for helpful conversations and comments on earlier drafts. The authors also thank Liz Sharkey for helpful research assistance. Finally, the authors thank the Carl S. Lloyd Faculty Fund for research support.

Lior Jacob Strahilevitz
Sidley Austin Professor of Law, The University of Chicago Law School.
Imagine a person is being questioned by the police. If this is a mere friendly chat, then the police need not advise that person of her rights. If, however, this is a “custodial interrogation,” then the person—the suspect—must generally be given a Miranda warning for any incriminating statements she makes to be admissible in court.
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86.2
Big Data and Discrimination
Talia B. Gillis
Doctoral Student, Harvard Business School; John M. Olin Fellow in Empirical Law and Economics, Harvard Law School.

For helpful feedback, we thank Oren Bar-Gill, Netta Barak-Corren, Yochai Benkler, John Beshears, Alexandra Chouldechova, Ellora Derenoncourt, Noah Feldman, Deborah Hellman, Howell Jackson, Cass Sunstein, Justin Wolfers, the editors and the participants of The University of Chicago Law Review Symposium on Personalized Law, and participants of the Law and Economics Colloquium at the University of Toronto and the Business Law Workshop at Oxford University. Talia Gillis acknowledges support provided by the John M. Olin Center for Law, Economics, and Business at Harvard Law School.

Jann L. Spiess
Post-doctoral Researcher, Microsoft Research New England.

For many financial products, such as loans and insurance policies, companies distinguish between people based on their different risks and returns. However, the ability to distinguish between people by trying to predict future behavior or profitability of a contract is often restrained by legal rules that aim to prevent certain types of discrimination.

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86.2
Personalizing Precommitment
Lee Anne Fennell
Max Pam Professor of Law, The University of Chicago Law School.

I thank Ellen Aprill, Adam Hirsch, and participants in The University of Chicago Law Review Symposium on Personalized Law and in the 2018 Annual Meeting of the American Law and Economics Association for helpful comments and questions. Research support from the Harold J. Green Faculty Fund and the SNR Denton Fund is also gratefully acknowledged. Some of the analysis contained here will appear in Lee Anne Fennell, Slices and Lumps: Division and Aggregation in Law and Life (Chicago, forthcoming 2019).

Many people experience self-control problems in domains from saving money to losing weight.

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Article
86.2
The Chilling Effect of Governance-by-Data on Data Markets
Niva Elkin-Koren
Professor, University of Haifa Faculty of Law; Director, Center for Cyber Law and Policy, University of Haifa; Faculty Associate, Berkman Klein Center for Internet & Society, Harvard University.

We would like to thank Rabeea Assy, Harry First, Eleanor Fox, Tamar Indig, Marcel Kahan, Yafit Lev-Aretz, Hans-Wolfgang Micklitz, Alan Miller, Ariel Porat, Daniel Richman, Eden Sarid, Catherine Sharkey, Katherine Strandburg, Alina Wernick, participants of the Competition, Innovation, and Information Law (CIIL) Speakers Series and the Privacy Research Group at NYU School of Law, and The University of Chicago Law Review Symposium on Personalized Law for most thoughtful comments and discussions. Thanks to Ilana Atron, Saar Ben Zeev, and Lior Frank for most helpful research assistance. This research was supported by the Center for Cyber Law and Policy, University of Haifa. Any mistakes or omissions are the authors’.

Michal S. Gal
Professor and Director of the Forum for Law and Markets, University of Haifa Faculty of Law; President, Academic Society for Competition Law (ASCOLA).

Big data has become an important resource not only in the commercial sphere but also in the legal one. Governance-by-data can take many forms, including setting enforcement priorities, affecting methods of proof, and even changing the content of legal norms.

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86.2
Neuroscience and the Personalization of Criminal Law
Deborah W. Denno
Arthur A. McGivney Professor of Law, Founding Director, Neuroscience and Law Center, Fordham University School of Law. All statistics and case distributions discussed in this Essay and presented in the Appendix in Table 1 and Figures 1–5 are organized and on file with the author and with The University of Chicago Law Review.

I am most grateful to the following persons for their contributions to this Essay: Ruben Coen-Cagli, Nestor Davidson, Kathleen Ellis, Janet Freilich, Marianna Gebhardt, David Greenberg, Filippo Maria Lancieri, Jacob Nadler, Mark Patterson, Richard Squire, Ryan Surujnath, and Erica Valencia-Graham. I also thank Alissa Black-Dorward and the Fordham University School of Law library staff for superb research assistance as well as Timothy W. DeJohn for providing information about the Jones case. I received insightful comments on earlier versions of this Essay from the participants in presentations given at Stanford Law School (the BioLawLapalooza 2.0), the Fordham University School of Law, and at The University of Chicago Law Review Symposium on Personalized Law (organized by Omri Ben-Shahar and Ariel Porat). I am indebted to five sources for research funding, without which this project could not have existed: Fordham University School of Law, the Fordham Neuroscience and Law Center, the Gerald Adelman Fellowship, Roger Sacks, and the Barnet and Sharon Phillips Family Fund. No individual or organization acknowledged in this Essay necessarily supports the Essay’s interpretations or conclusions. Responsibility for any mistakes or misjudgments rests solely with the author.

Every criminal case is part of a larger personal story—some headline-grabbing, some entirely mundane; yet each narrative is important to how the criminal justice system assesses an individual’s level of culpability.

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86.2
A Framework for the New Personalization of Law
Anthony J. Casey
Professor of Law, The University of Chicago Law School.

I am grateful for research support from the Richard Weil Faculty Research Fund and the Paul H. Leffman Fund. Stephanie Xiao and Courtney Block provided excellent research assistance.

Anthony Niblett
Associate Professor and Canada Research Chair in Law, Economics, & Innovation, University of Toronto Faculty of Law. In the interests of full disclosure, Professor Niblett is also a cofounder of Blue J Legal, a start-up bringing machine learning to the law.

Personalized law is an old concept. The idea that the law should be tailored to better fit the relevant context to which it applies is obvious and has been around as long as the idea of law itself.

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86.2
Implementing Personalized Law: Personalized Disclosures in Consumer Law and Data Privacy Law
Christoph Busch
Professor of Law at the University of Osnabrück, Germany.

I am grateful to the participants of The University of Chicago Law Review Symposium on Personalized Law organized by Omri Ben-Shahar, Anthony Casey, Ariel Porat, and Lior Strahilevitz in April 2018 for their very helpful comments and suggestions. I would also like to thank Alberto De Franceschi and the participants of the conference on Granular Legal Norms at Villa Vigoni in March 2017 for their insightful inputs.

Mandated disclosures are probably one of the most widely used regulatory tools in consumer law and data privacy law on both sides of the Atlantic.

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