On Classifying the outcomes of Legal Motions

Abstract

Conflict is inherent to the human condition, and socially acceptable methods of resolving conflict typically begin with dialogue, compromise, or negotiation. When these efforts fail, the legal process, often culminating in the courtroom, becomes the final recourse. Legal practitioners strive to position themselves advantageously by predicting the outcomes of legal disputes, increasingly relying on predictive tools to navigate the complexities of the courtroom.

This thesis investigates the feasibility of predicting the outcomes of legal motion disputes using supervised machine learning methods. While previous research has predominantly utilized expertly hand-crafted features for judicial predictions, this study explores the use of written arguments, known as briefs, as the only basis for prediction. We trained 36 classifiers to predict the outcomes of legal motions and compared their performance to that of a baseline model.

The best-performing classifier achieved an accuracy of 62\% on the test dataset. However, statistical analysis reveals that the performance of the top 10 classifiers is not statistically different from the baseline model. These findings suggest that, among the top-performing classifiers, there is no conclusively dominant approach for predicting legal motion outcomes using briefs. The thesis also offers theoretical considerations to explain these results.