What Matters: Agreement between U.S. Courts of Appeals Judges

Chen, Daniel L.; Cui, Xing; Shang, Lanyu; Zheng, Junchao

doi:10.2139/ssrn.2816492

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What Matters: Agreement between U.S. Courts of Appeals Judges

Journal of Machine Learning Research (W&CP), 2016

7 Pages Posted: 2 Aug 2016 Last revised: 20 Apr 2020

See all articles by Daniel L. Chen

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

Xing Cui

New York University (NYU) - Center for Data Science

Lanyu Shang

New York University (NYU) - Center for Data Science

Junchao Zheng

New York University (NYU) - Center for Data Science

Date Written: July 31, 2016

Abstract

Federal courts are a mainstay of the justice system in the United States. In this study, we analyze 387,898 cases from U.S. Courts of Appeals, where judges are randomly assigned to panels of three. We predict which judge dissents against co-panelists and analyze the dominant features that predict such dissent with a particular attention to the biographical features that judges share. Random forest achieves the best classification. Dissent is predominantly driven by case features, though personal features also predict agreement.

Keywords: Machine Learning, Circuit Court, Judgment Prediction, Dissent, Data Science

Suggested Citation: Suggested Citation

Chen, Daniel L. and Cui, Xing and Shang, Lanyu and Zheng, Junchao, What Matters: Agreement between U.S. Courts of Appeals Judges (July 31, 2016). Journal of Machine Learning Research (W&CP), 2016, Available at SSRN: https://ssrn.com/abstract=2816492 or http://dx.doi.org/10.2139/ssrn.2816492