Predictive Analytics in Judicial Decision-Making: Between Statistical Modeling, Algorithmic Prediction, and the Limits of Legal Reasoning

This paper examines the growing use of predictive analytics in judicial decision-making, focusing on the role of statistical models and algorithmic systems in forecasting legal outcomes. It critically explores the tension between data-driven prediction and traditional legal reasoning grounded in judicial discretion and case-specific analysis. The study argues that while predictive analytics can enhance consistency, efficiency, and access to legal information, it also raises significant concerns regarding fairness, bias, and the potential standardization of judicial outcomes. It highlights the risks of over-reliance on statistical correlations that may not adequately capture the normative and contextual dimensions of legal decision-making. Adopting a legal-analytical approach, the research evaluates the epistemological limits of algorithmic prediction in law and proposes a balanced framework that integrates predictive tools with judicial reasoning. This framework emphasizes transparency, interpretability, and human oversight to ensure that predictive systems remain supportive rather than determinative. The paper contributes to Legal Tech scholarship by redefining the role of predictive analytics within judicial systems, offering a structured perspective that aligns technological innovation with the preservation of judicial independence and legal reasoning.