
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.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 6 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
