Automated Classification of Modes of Moral Reasoning in Judicial Decisions

Abstract

What modes of moral reasoning do judges employ? We construct a linear SVM classifier for moral reasoning mode trained on applied ethics articles written by consequentialists and deontologists. The model can classify a paragraph of text in held out data with over 90 percent accuracy. We then apply this classifier to a corpus of circuit court opinions. We show that the use of consequentialist reasoning has increased over time. We report rankings of relative use of reasoning modes by legal topic, by judge, and by judge law school.

Replaced by

Nischal Mainali, Liam Meier, Elliott Ash, and Daniel L. Chen, “Automated Classification of Modes of Moral Reasoning in Judicial Decisions”, in Computational Legal Studies: The Promise and Challenge of Data-Driven Research, Ryan Whalen (ed.), 2020.

Reference

Elliott Ash, Daniel L. Chen, Nischal Mainali, and Liam Meier, “Automated Classification of Modes of Moral Reasoning in Judicial Decisions”, IAST Working Paper, n. 18-92, December 2018.