Adaptive Sentencing Prediction with Guaranteed Accuracy and Legal Interpretability

• URL: http://arxiv.org/abs/2505.14011v1
• Date: Tue, 20 May 2025 07:06:00 GMT
• Title: Adaptive Sentencing Prediction with Guaranteed Accuracy and Legal Interpretability
• Authors: Yifei Jin, Xin Zheng, Lei Guo,
• Abstract summary: We propose a novel Saturated Mechanistic Sentencing (SMS) model, which provides inherent legal interpretability.<n>We also introduce the corresponding Least Momentum Mean Squares (MLMS) adaptive algorithm for this model.<n>We provide a best possible upper bound for the prediction accuracy by the best predictor designed in the known parameters case.
• Score: 7.737114256060652
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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