Adaptive Sentencing Prediction with Guaranteed Accuracy and Legal Interpretability

TL;DR

This paper introduces an interpretable legal sentencing prediction model based on China's Criminal Law, with a novel adaptive algorithm and theoretical accuracy guarantees, validated on a real-world dataset.

Contribution

The paper presents a new interpretable sentencing model, an adaptive algorithm with proven accuracy bounds, and a real-world dataset for Chinese legal sentencing prediction.

Findings

The model achieves near-optimal prediction accuracy.

The adaptive algorithm's accuracy is theoretically guaranteed.

Experiments validate the model's practical effectiveness.

Abstract

Existing research on judicial sentencing prediction predominantly relies on end-to-end models, which often neglect the inherent sentencing logic and lack interpretability-a critical requirement for both scholarly research and judicial practice. To address this challenge, we make three key contributions:First, we propose a novel Saturated Mechanistic Sentencing (SMS) model, which provides inherent legal interpretability by virtue of its foundation in China's Criminal Law. We also introduce the corresponding Momentum Least Mean Squares (MLMS) adaptive algorithm for this model. Second, for the MLMS algorithm based adaptive sentencing predictor, we establish a mathematical theory on the accuracy of adaptive prediction without resorting to any stationarity and independence assumptions on the data. We also provide a best possible upper bound for the prediction accuracy achievable by the best predictor designed in the known parameters case. Third, we construct a Chinese Intentional Bodily Harm (CIBH) dataset. Utilizing this real-world data, extensive experiments demonstrate that our approach achieves a prediction accuracy that is not far from the best possible theoretical upper bound, validating both the model's suitability and the algorithm's accuracy.