From at least Leibniz, the dream of removing human beings from the loop of legal reasoning has captured the imaginations of philosophers, lawyers, and (more recently) computer scientists. This project of law-as-computation (sometimes referred to as “computational law”) seeks to reduce the law to a set of algorithms that could be automatically executed on a computer, seamlessly translating raw inputs into legal conclusions. Proponents of this approach generally argue that legal automation would would increase legal certainty and facilitate the neutral application of law by transcending human biases and errors. This paper describes the theory behind law-as-computation, discusses a particularly promising approach based on recent advances in machine learning, and examines the normative desirability of removing humans from the task of legal interpretation. The paper finds that the strongest set of objections to law-and-computation derive from the participation rights of legal subjects. Whether or not participation rights should override the potential benefits of law-as-computation remains an open question.

Michael A. Livermore, Rule by Rules, in Computational Legal Studies: The Promise and Challenge of Data-Driven Research, Edward Elgar, 238–264 (2020).