Document Type


Publication Date



In Personalized Law: Different Rules for Different People, Omri Ben-Shahar and Ariel Porat undertake to ground a burgeoning field of legal thought. The book imagines and thoughtfully assesses an array of personalized legal rules, including individualized speed limits, fines calibrated to income, and medical disclosure requirements responsive to individual health profiles. Throughout, though, the conceptual parameters of “personalized law” remain elusive. It is clear that personalized law involves more data, more machine-learning, and more direct communication to individuals. But it is not clear how deep these changes go. Are they incremental—just today’s law with better tech—or do they represent a change in the very nature of legal rules, as the authors claim?

This Essay aims to help clarify the concept of “personalized law” by starting from the nature of legal rules. Drawing on the scholarship of Frederick Schauer, it argues that all rules must generalize in some way, then offers a taxonomy of different forms of generalization. With the framework in place, it becomes clear that personalized law entails two shifts: (1) from rules that prescribe specific conduct toward rules that prescribe a social outcome, like a risk or efficiency target, and (2) toward greater ex ante specification of what rules require of individuals. Each shift has different practical and normative implications. Lastly, the Essay raises two potential costs of such shifts that Personalized Law does not address at length: the likely disparate racial impact of social-outcome rules driven by big data, and the possible loss of communal experience in shared-rule compliance.


legal rules, individual determinations, machine learning, algorithmic decisionmaking, individualization, customization, generalization, reasonable person standard

Publication Title

University of Chicago Law Review Online

Publication Citation

2022 U. Chi. L. Rev. Online (2022)