Logic-Guided Multistage Inference for Explainable Multidefendant Judgment Prediction
This work addresses the challenge of fairness and precision in AI-driven judicial systems for multidefendant cases, though it is incremental in applying existing methods to a specific legal domain.
The paper tackles the problem of predicting judgments in multidefendant criminal cases by incorporating sentencing logic into a Transformer framework to improve role differentiation and interpretability, achieving significant accuracy improvements on a custom dataset for intentional injury cases.
Crime disrupts societal stability, making law essential for balance. In multidefendant cases, assigning responsibility is complex and challenges fairness, requiring precise role differentiation. However, judicial phrasing often obscures the roles of the defendants, hindering effective AI-driven analyses. To address this issue, we incorporate sentencing logic into a pretrained Transformer encoder framework to enhance the intelligent assistance in multidefendant cases while ensuring legal interpretability. Within this framework an oriented masking mechanism clarifies roles and a comparative data construction strategy improves the model's sensitivity to culpability distinctions between principals and accomplices. Predicted guilt labels are further incorporated into a regression model through broadcasting, consolidating crime descriptions and court views. Our proposed masked multistage inference (MMSI) framework, evaluated on the custom IMLJP dataset for intentional injury cases, achieves significant accuracy improvements, outperforming baselines in role-based culpability differentiation. This work offers a robust solution for enhancing intelligent judicial systems, with publicly code available.