Explainable Predictive Jurisprudence for Anticipating Legal Doctrine Evolution in Dynamic Judicial Systems

Abstract

Legal systems evolve continuously in response to legislative reforms, emerging judicial interpretations, and shifting societal expectations, making it increasingly difficult to anticipate changes in legal precedent using conventional analytical methods. This study introduces an explainable artificial intelligence framework for predictive jurisprudence that captures the temporal evolution of legal reasoning by jointly modeling semantic, structural, and causal relationships within judicial decisions. The proposed framework integrates neural temporal graph networks to learn evolving citation dependencies, dynamic topic modeling to characterize changes in legal doctrines over time, and causal-inference techniques to distinguish genuine jurisprudential influence from spurious associations. To enhance transparency, the predictive process is complemented by GNNExplainer, enabling the identification of the legal principles, precedents, and citation patterns that most strongly influence model predictions. The framework is evaluated using the Free Law Project and LePaRD benchmark datasets and demonstrates superior performance over existing approaches in detecting causal judicial influences and accurately quantifying precedent evolution. Its practical applicability and interpretability are further validated through expert legal assessment and historical backtesting against documented jurisprudential shifts. The experimental findings demonstrate that integrating explainable machine learning with causal legal analytics provides reliable early indicators of doctrinal change, offering valuable decision-support capabilities in legal environments.