Citation :

Enas Mohamed Ali Quteishat, Ahmed Qtaishat, "Artificial Intelligence for Judicial Decision Support: Predicting Supreme Court Outcomes," International Journal of Electronics and Communication Engineering, vol. 13, no. 6, pp. 38-49, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I6P104

Abstract

The latest developments in Artificial Intelligence (AI) and Machine Learning (ML) offer new possibilities to assist legal professionals with complex decision-making, particularly by developing AI-based applications for judicial decision support (that is, non-automated methods of providing data-driven insights to support judicial decision-making). This research investigates the effectiveness of AI in predictive modeling for judicial decision support. There is a growing body of literature concerning the effectiveness of using ML models to predict Supreme Court outcomes. Using SCOTUS decisions as a large-scale dataset, we provide a comprehensive comparative analysis comparing multiple ML algorithms to predict Supreme Court case outcomes using both individual (e.g., Decision Tree, Naive Bayes, SVC, Linear SVC, kNN, RF, ET, GBM, and AdaBoost) and ensemble-based classifiers. To assess model performance, we use accuracy, precision, recall, and F1 score since these statistics provide a balanced assessment when the classes are imbalanced. We demonstrate that ensemble learning models outperform individual classifiers, while boosting models excel in predictive accuracy and balanced classification performance. Consequently, our findings suggest AI has considerable potential as a judicial decision support tool to improve decision-making with greater efficiency, consistency, and informed reasoning while also preserving sufficient human oversight to ensure fairness and ethical accountability.

Keywords

Artificial Intelligence, Judicial Decision Support, Machine Learning, Supreme Court, Legal Analytics, Predictive Modelling, LegalTech.

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