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
Date Written: July 31, 2016
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
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