Rules operate as a tool of governance by making generalizations, thereby cutting down on government officials’ need to make individual determinations. But because they are generalizations, rules can result in inefficient or perverse outcomes due to their over- and under-inclusiveness. With the aid of advances in machine-learning algorithms, however, it is becoming increasingly possible to imagine governments shifting away from a predominant reliance on general rules and instead moving toward increased reliance on precise individual determinations—or on “personalized law,” to use the term Omri Ben-Shahar and Ariel Porat use in the title of their 2021 book. Among the various technological, organizational, and political hurdles that stand in the way of a personalized system of law, I elaborate three obstacles that I refer to as the challenges of completeness, consensus, and currency. I then offer two solutions—custom and competence—that could bring about public acceptance to personalized law. Although I do not envision that these solutions can be complete ones, in the sense that they cannot prevent all problems with personalized law, a system of personalized law need not be perfect to be normatively appealing. All that personalized law must be is better than the imperfect rule-based system in place today.
Administrative law, rule-based governance, individual determinations, machine learning, algorithmic decisionmaking, public administration, individualization, customization, goal-based regulation, artificial intelligence, digital government, performance standards, law and technology, computers
University of Chicago Law Review Online
Coglianese, Cary, "Moving Toward Personalized Law" (2022). Faculty Scholarship at Penn Carey Law. 2819.
Administrative Law Commons, Artificial Intelligence and Robotics Commons, Public Administration Commons, Public Law and Legal Theory Commons, Science and Technology Studies Commons
U. Chi. L. Rev. Online, forthcoming