Incorporating Legal Logic into Deep Learning: An Intelligent Approach to Probation Prediction

TL;DR

This paper introduces a novel deep learning model that incorporates legal logic for probation prediction, enhancing accuracy and aligning judicial decision-making with legal principles.

Contribution

It presents a new legal-logic-integrated deep learning model and a specialized probation dataset, advancing judicial AI with a focus on legal reasoning.

Findings

MT-DT outperforms baseline models in accuracy

Legal logic integration improves model interpretability

Dataset includes detailed legal and factual information

Abstract

Probation is a crucial institution in modern criminal law, embodying the principles of fairness and justice while contributing to the harmonious development of society. Despite its importance, the current Intelligent Judicial Assistant System (IJAS) lacks dedicated methods for probation prediction, and research on the underlying factors influencing probation eligibility remains limited. In addition, probation eligibility requires a comprehensive analysis of both criminal circumstances and remorse. Much of the existing research in IJAS relies primarily on data-driven methodologies, which often overlooks the legal logic underpinning judicial decision-making. To address this gap, we propose a novel approach that integrates legal logic into deep learning models for probation prediction, implemented in three distinct stages. First, we construct a specialized probation dataset that includes fact descriptions and probation legal elements (PLEs). Second, we design a distinct probation prediction model named the Multi-Task Dual-Theory Probation Prediction Model (MT-DT), which is grounded in the legal logic of probation and the \textit{Dual-Track Theory of Punishment}. Finally, our experiments on the probation dataset demonstrate that the MT-DT model outperforms baseline models, and an analysis of the underlying legal logic further validates the effectiveness of the proposed approach.