Unstable Personalized Law

Document Type

Article

Publication Date

6-2024

DOI

https://doi.org/10.1093/jrls/jlae006

Abstract

“Personalized Law”is a remarkable book in its scope and creativity, inviting readers to imagine a radically different world of customized legal rules while challenging our assumption that current legal rules are depersonalized. Whether taken as a practical guide for developing more effective and equitable legal rules or as a thought experiment questioning our current notions of legal commands, it provides insights into the relationship between legal design and the policies underlying those laws.

In this Response, I address one type of first-stage prediction imperfection — the instability of intrapersonal predictions across model iterations — and discuss its implications for personalized law. While some prediction error is a necessary property of classification and prediction methods, I argue that this error, as it pertains to an individual’s prediction, may not be stable over iterations of the prediction model. As I will demonstrate in a particular setting below, small changes to the training set used to predict a borrower credit risk can produce different risk scores despite the stability of the overall model accuracy measure. If the prediction and classification functions we use to produce individual scores are unstable, this means that legal rules at the second stage, when tailored to reflect and individual’s score or classification, will also be unstable. Decisions made by model designers can produce varying legal rules for individuals even at the initial stage of model development; however, my focus is on the instability of predictions over time.

Disciplines

Human Rights Law | Law | Law and Philosophy

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