Building a Predictive Model for Legal Studies through Ensemble Learning Techniques

Abstract

The emerging field of Artificial Intelligence (AI) has the potential to not only aid, but also transform and potentially replace human decision-making in a wide range of areas, including the legal system. The integration of computer science and law, exemplified using artificial intelligence in legal decision-making, improves the efficiency of handling cases and promotes standardization in legal procedures, while strengthening the organization of legal information. This paper expands on previous research in the field of judicial prediction and presents the first comprehensive, reliable, and applicable Machine Learning (ML) model for predicting decisions issued by the Supreme Court of the United States. This represents a notable progress in the field of predictive analytics. This work conduct a thorough and comparative analysis of prediction results for various algorithms, including Perceptron, Logistic Regression (LR), Support Vector Machines (SVMs), Naïve Bayes (NB), k-Nearest Neighbors (k-NN), Multi-Layer Perceptron (MLP), Calibrated, and Ensemble Learning. The implemented models showcase the ability to accurately predict the results of legal systems, especially by utilising Ensemble techniques. Proposed research explores the integration of different ML and Ensemble learning techniques in the field of legal studies, which is experiencing tremendous technological advancements. It discusses how this technology has the potential to significantly transform the judicial process. These capabilities can greatly enhance decision-making in complex legal situations. This manuscript envisions a future judicial system where the use of ML technology greatly improves the efficiency and fairness of delivering justice.