Logic-Guided Multistage Inference for Explainable Multidefendant Judgment Prediction

TL;DR

This paper introduces a logic-guided multistage inference framework using a Transformer model to improve explainability and accuracy in multidefendant judgment prediction, addressing role differentiation and culpability assessment.

Contribution

It proposes a novel MMSI framework that incorporates sentencing logic, role-oriented masking, and data strategies for better judicial AI interpretability and performance.

Findings

Achieves significant accuracy improvements over baselines.

Effectively differentiates roles of principals and accomplices.

Demonstrates robustness on the IMLJP dataset.

Abstract

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.