Public Law & Legal Theory
This paper provides the first comprehensive account of personalized default rules and personalized disclosure in the law. Under a personalized approach to default rules, individuals are assigned default terms in contracts or wills that are tailored to their own personalities, characteristics, and past behaviors. Similarly, disclosures by firms or the state can be tailored so that only information likely to be relevant to an individual is disclosed, and information likely to be irrelevant to her is omitted. The paper explains how the rise of Big Data makes the effective personalization of default rules and disclosure far easier than it would have been during earlier eras. The paper then shows how personalization might improve existing approaches to the law of consumer contracts, medical malpractice, inheritance, landlord-tenant relations, and labor law. The paper makes several contributions to the literature. First, it shows how data mining can be used to identify particular personality traits in individuals, and these traits may in turn predict preferences for particular packages of legal rights. Second, it proposes a regime whereby a subset of the population (“guinea pigs”) is given a lot of information about various contractual terms and plenty of time to evaluate their desirability, with the choices of particular guinea pigs becoming the default choices for those members of the general public who have similar personalities, demographic characteristics, and patterns of observed behavior. Third, we assess a lengthy list of drawbacks to the personalization of default rules and disclosure, including cross-subsidization, strategic behavior, uncertainty, stereotyping, privacy, and institutional competence concerns. Finally, we explain that the most trenchant critiques of the disclosure strategy for addressing social ills are really criticisms of impersonal disclosure. Personalized disclosure not only offers the potential to cure the ills associated with impersonal disclosure strategies, but it can also ameliorate many of the problems associated with the use of personalized default rules.
Lior Strahilevitz & Ariel Porat, "Personalizing Default Rules and Disclosure with Big Data" (University of Chicago Public Law & Legal Theory Working Paper No. 418, 2013).