Automated Classification of Modes of Moral Reasoning in Judicial Decisions

Computational Legal Studies, Forthcoming

14 Pages Posted: 29 Jun 2018 Last revised: 26 Dec 2018

See all articles by Nischal Mainali

Nischal Mainali

New York University (NYU) - New York University Abu Dhabi

Liam Meier

New York University (NYU) - New York University Abu Dhabi

Elliott Ash

ETH Zurich

Daniel L. Chen

Directeur de Recherche, Centre National de la Recherche Scientifique, Toulouse School of Economics, Institute for Advanced Study in Toulouse, University of Toulouse Capitole, Toulouse, France

Date Written: July 2, 2018

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.

Suggested Citation

Mainali, Nischal and Meier, Liam and Ash, Elliott and Chen, Daniel L., Automated Classification of Modes of Moral Reasoning in Judicial Decisions (July 2, 2018). Computational Legal Studies, Forthcoming. Available at SSRN: https://ssrn.com/abstract=3205286 or http://dx.doi.org/10.2139/ssrn.3205286

Nischal Mainali

New York University (NYU) - New York University Abu Dhabi ( email )

PO Box 129188
Abu Dhabi
United Arab Emirates

Liam Meier

New York University (NYU) - New York University Abu Dhabi ( email )

PO Box 129188
Abu Dhabi
United Arab Emirates

Elliott Ash

ETH Zurich ( email )

Rämistrasse 101
ZUE F7
Zürich, 8092
Switzerland

Daniel L. Chen (Contact Author)

Directeur de Recherche, Centre National de la Recherche Scientifique, Toulouse School of Economics, Institute for Advanced Study in Toulouse, University of Toulouse Capitole, Toulouse, France ( email )

21 allée de Brienne
31015 Toulouse cedex 6 France
Toulouse, 31015
France

Register to save articles to
your library

Register

Paper statistics

Downloads
15
Abstract Views
165
PlumX Metrics