Automated Classification of Modes of Moral Reasoning in Judicial Decisions

Nischal Mainali (), Liam Meier (), Elliott Ash and Daniel L. Chen ()
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Nischal Mainali: NYU - New York University [New York] - NYU - NYU System
Liam Meier: NYU - New York University [New York] - NYU - NYU System
Daniel L. Chen: TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, CNRS - Centre National de la Recherche Scientifique

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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.

Date: 2023-07-17
Note: View the original document on HAL open archive server: https://hal.science/hal-04163443v1
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Related works:
Working Paper: Automated Classification of Modes of Moral Reasoning in Judicial Decisions (2018)
Working Paper: Automated Classification of Modes of Moral Reasoning in Judicial Decisions (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:wpaper:hal-04163443

DOI: 10.2139/ssrn.3205286

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