Machines Finding Injustice

Authors

Hannah S. Laquer, University of California, DavisFollow
Ryan W. Copus, University of Missouri-Kansas CityFollow

ORCID (Links to author’s additional scholarship at ORCID.org)

Hannah S. Laquer: https://orcid.org/0000-0001-5331-8627

Abstract

With rising caseloads, review systems are increasingly taxed, stymieing traditional methods of case screening. We propose an automated solution: predictive models of legal decisions can be used to identify and focus review resources on outlier decisions—those decisions that are most likely the product of biases, ideological extremism, unusual moods, and carelessness and thus most at odds with a court’s considered, collective judgment. By using algorithms to find and focus human attention on likely injustices, adjudication systems can largely sidestep the most serious objections to the use of algorithms in the law: that algorithms can embed racial biases, deprive parties of due process, impair transparency, and lead to “technological–legal lock-in.”

Recommended Citation

Hannah S. Laquer & Ryan W. Copus, Machines Finding Injustice, 23 SMU Sci. & Tech. L. Rev. 151 (2020)