Predictive Modelling in Legal Decision-Making: Leveraging Machine Learning for Forecasting Legal Outcomes

Abstract

 Predictive modelling holds significant promise in enhancing legal decision-making processes, particularly within the realm of the Supreme Court of the United States (SCOTUS). This paper investigates the application of Machine Learning (ML) algorithms to forecast legal outcomes, utilizing a dataset comprising SCOTUS cases. Through rigorous preprocessing and analysis, various ML techniques including Decision Trees, Random Forest, Support Vector Machines (SVM), Naive Bayes, k-Nearest Neighbors (k-NN), and XGBoost are applied. The performance of these models is evaluated using precision, recall, F1-score, and accuracy metrics, revealing nuanced differences in their effectiveness. Notably, XGBoost emerges as the top-performing algorithm with an accuracy of 72%, showcasing its robustness in capturing intricate legal patterns. In contrast, Naive Bayes and Decision Tree algorithms exhibit lower accuracies of 61% and 52%, respectively, highlighting potential limitations in their applicability to legal datasets. The comparative analysis sheds light on the strengths and weaknesses of each algorithm, underscoring the importance of selecting appropriate techniques tailored to the complexities of legal decision-making. This study contributes to the growing body of literature on predictive modelling in legal studies, offering valuable insights into the potential applications and implications of ML in enhancing the efficiency and efficacy of legal processes.