Explainable AI in Judicial Decision-Making: A Hybrid LegalTech Approach

The development of Artificial Intelligence (AI) for inclusion in legal systems around the world has accelerated significantly and provides opportunities for improving the efficiency, consistency, and objectivity of legal decisions. Yet, in high-stakes legal environments, incorporating AI raises vital questions surrounding legal transparency, accountability, and interpretability, primarily where an individual's rights or their life is impacted by the AI decision. This paper proposes a hybrid legal-technology (LegalTech) framework that integrates machine learning models with Explainable AI (XAI) approaches to facilitate decision support within legal systems transparently and interpretably. The suggested framework uses Shapley Additive Explanations (SHAP) and rule-based reasoning to support the accuracy of AI-generated legal recommendations and their understandability to humans. Experimental evaluation on three established legal datasets—the European Court of Human Rights (ECHR) dataset, the COMPAS recidivism dataset, and the Contract Understanding Atticus Dataset (CUAD)—shows that the hybrid approach achieves competitive predictive accuracy (86.4% accuracy, 85.3% F1-Score, 0.91 AUC) while significantly increasing the interpretability of the resulting models versus traditional black-box methods. The analysis demonstrates that the combined AI and XAI framework enables judges and other legal professionals to access evidence-based, explainable, and reliable insights while retaining human authority over final decisions. This study contributes to the evolving area of responsible artificial intelligence in law and provides a roadmap for creating trustworthy, transparent, and just AI-supported legal decision-making.