29 Jerusalem Review of Legal Studies 65
This Article discusses Omri Ben-Shahar and Ariel Porat’s book “Personalized Law,” which offers an exploration of the concept of customizing legal rules to fit individual circumstances and characteristics. The book focuses primarily on the second stage of implementing personalized law—how to use classification models to tailor legal rules to individual profiles. This Article focuses on the challenges in the first stage of personalized law—the creation of individual classification models—and the inherent challenges of such a prediction and classification exercise. The Article addresses a critical aspect of these challenges: the instability of intrapersonal predictions across model iterations. While prediction error is a known characteristic of machine learning models, the focus here is on the variability of individual predictions over time, despite overall model accuracy. This instability can lead to fluctuating legal rules for individuals, creating uncertainty and potentially undermining the effectiveness of personalized law. Current legal systems already exhibit some instability, but personalized law could amplify this issue, particularly in areas with cumulative impacts like privacy preferences or consent age.
I demonstrate the intrapersonal instability of machine learning predictions through the example of predicting credit default. The implications for personalized law are significant: individuals could face varying legal rules without changes in their circumstances, and such instability could erode the reliability and effectiveness of legal frameworks.
I end by introducing a framework for assessing the costs of instability, mirroring the accuracy-fairness tradeoff. Here, a stability-accuracy tradeoff emerges, where the accuracy of predictions can come at the expense of intrapersonal prediction stability. This framework underscores the importance of considering stability as a competing interest in designing personalized legal systems. By focusing on this tension, the article contributes to the broader discourse on the feasibility and implications of personalized law, emphasizing the need for a balanced approach that acknowledges both the potential benefits and inherent limitations of algorithmic personalization in legal settings.