An AI-Based Decision Support System Utilizing Bayesian Networks for Judicial Decision-Making

Abstract

Judicial decision-making in continental law systems requires carefully evaluating complex, interdependent evidence while ensuring consistency and fairness. This study investigates the application of Bayesian networks in structuring legal evidence within an AI-based decision support system. The primary research objective is to assess the enhancement of transparency, minimization of cognitive overload, and reduced bias in judicial processes through probabilistic reasoning. The proposed system dynamically updates outcome probabilities as new evidence is introduced, enabling real-time monitoring of case likelihoods. AI-generated recommendations are aligned with judicial precedents by integrating Conditional Probability Tables (CPTs) and historical case data in this adaptive approach. The interpretability and effectiveness of Bayesian inference in legal decision support are analyzed methodologically, emphasizing its capacity to refine probability distributions in response to evolving courtroom inputs. The findings address a key research gap by demonstrating how structured AI-driven heuristics can supplement judicial reasoning while maintaining decision accountability and transparency. The system is suggested to enhance consistency and fairness in legal judgments while preserving judicial autonomy. This study contributes to the growing intersection of AI and legal decision-making, with an emphasis placed on the role of machine learning in supporting judicial heuristics while maintaining procedural integrity.