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Separation, Pooling, and Big Data

49 Pages Posted: 5 Sep 2015 Last revised: 15 Apr 2016

James C. Cooper

George Mason University - Antonin Scalia Law School, Faculty

Date Written: March 14, 2016

Abstract

Privacy is about being “let alone,” so in one sense, privacy means to separate yourself from the world. Paradoxically, by concealing facts about yourself, observers view you as less separated from everyone else. They can no longer make out the features that distinguish you from those to whom you bear a superficial resemblance. In this manner, privacy promotes what economists call “pooling.” Markets, however, tend to benefit from “separation” — the ability to distinguish between different types. This tension between privacy and market efficiency — between pooling and separation — is on full display in the burgeoning privacy law scholarship surrounding big data, which has centered on so-called “predictive privacy harms.” This scholarship has begun to seep into policy discussions, leading to proposals to limit the ability of firms to use big data. Privacy without a doubt is valuable. It’s woven into the fabric of our society. But we must be careful to discern between privacy’s intrinsic and strategic values before prescribing drastic ex ante restrictions to address predictive privacy harms. The major contribution of this paper is to develop a positive framework based on the economics of contracts and torts to identify when limiting big data predictions may be justified. This framework suggests that when strategic privacy is at issue, the mechanisms should be rooted in antidiscrimination law — which embody the choices that society has made about which traits are fair game for classification — rather than privacy law. Alternatively, privacy law should be used when intrinsic privacy is implicated. The analysis suggests that ex ante restrictions on use make sense only in the narrow circumstances in which there is likely to be agreement that the big data predictions implicate highly sensitive information. Alternatively, when there is little agreement on how privacy harms are likely to be suffered, the default regulatory posture should be one of notice of collection and use, with the Federal Trade Commission enforcing a firm’s promises.

Keywords: big data, market efficiency, predictive privacy harms, privacy, pooling, regulation, separation

JEL Classification: D82

Suggested Citation

Cooper, James C., Separation, Pooling, and Big Data (March 14, 2016). George Mason Legal Studies Research Paper No. LS 15-15; George Mason Law & Economics Research Paper No. 15-32. Available at SSRN: https://ssrn.com/abstract=2655794 or http://dx.doi.org/10.2139/ssrn.2655794

James C. Cooper (Contact Author)

George Mason University - Antonin Scalia Law School, Faculty ( email )

3301 Fairfax Drive
Arlington, VA 22201
United States
703-993-9582 (Phone)

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