基于因果图分析的可解释司法判决预测方法研究

张虎; 张振; 范越; 郭佳钰

doi:10.11959/j.issn.2096-0271.2024023

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基于因果图分析的可解释司法判决预测方法研究

研究 | 更新时间：2024-06-03

- 基于因果图分析的可解释司法判决预测方法研究
- Research on interpretable legal judgment prediction method based on causal graph analysis
- 大数据 2024年10卷第2期页码：109-121
- 作者机构：
- 作者简介：
  
  [ "张虎（1979- ），男，博士，山西大学计算机与信息技术学院教授，山西大学计算机与信息技术学院（大数据学院）副院长，主要研究方向为自然语言处理、大数据挖掘与分析。" ]
  [ "张振（1998- ），男，山西大学计算机与信息技术学院硕士生，主要研究方向为自然语言处理。" ]
  [ "范越（1997- ），男，山西大学计算机与信息技术学院博士生，主要研究方向为自然语言处理。" ]
  [ "郭佳钰（1996- ），女，山西大学计算机与信息技术学院硕士生，主要研究方向为自然语言处理。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(62176145)
- DOI：10.11959/j.issn.2096-0271.2024023
  中图分类号： TP18
- 网络出版日期：2024-03，
  
  纸质出版日期：2024-03-15
- 稿件说明：
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张虎, 张振, 范越, 等. 基于因果图分析的可解释司法判决预测方法研究[J]. 大数据, 2024,10(2):109-121.

Hu ZHANG, Zhen ZHANG, Yue FAN, et al. Research on interpretable legal judgment prediction method based on causal graph analysis[J]. Big data research, 2024, 10(2): 109-121.
张虎, 张振, 范越, 等. 基于因果图分析的可解释司法判决预测方法研究[J]. 大数据, 2024,10(2):109-121. DOI： 10.11959/j.issn.2096-0271.2024023.

Hu ZHANG, Zhen ZHANG, Yue FAN, et al. Research on interpretable legal judgment prediction method based on causal graph analysis[J]. Big data research, 2024, 10(2): 109-121. DOI： 10.11959/j.issn.2096-0271.2024023.

摘要

随着人工智能技术的发展和海量司法数据的公开，面向“智慧司法”服务的司法判决预测（legal judgment prediction，LJP）任务受到了学术界和工业界的广泛关注，该任务旨在根据有限的案件事实描述文本来预测案件的罪名、法条和刑期。然而，现有工作缺乏对易混淆司法案件的智能决策的研究，且相关模型通常缺乏可解释性，这会导致模型预测严重依赖领域专家，阻碍LJP在不同法律体系中的应用。为此，提出了一种基于因果图分析的司法判决预测（prediction of legal judgment based on causal graph analysis， CGLJ）方法，首先从非结构化的法律事实描述文本中挖掘要素之间的因果关系，然后采用易混淆罪名聚类的构图方法构建因果图，既考虑了相似事实描述之间的差异，又增强了事实描述和法律法规之间的相互作用，最后将构建好的因果图融入深度神经网络进行联合推理，得到判决预测结果。此外，还对模型预测过程中的因果图推理过程进行了可视化，为判决结果提供了更好的可解释性。在2018中国“法研杯”司法人工智能挑战赛（CAIL2018）司法判决预测数据集上的实验结果表明，该方法相比基线模型取得了更好的效果。

Abstract

With the development of artificial intelligence technology and the disclosure of massive judicial data

the LJP task for ＆quot;smart justice＆quot; services has received widespread attention from academia and industry.The task aims to predict the charges

laws

and sentences of a case based on limited factual descriptions of the text.However

existing work lacks research on intelligent decision-making in easily confusing judicial cases

and related models often lack interpretability

which leads to heavy reliance on domain experts for model predictions and hinders the application of LJP in different legal systems.To this end

this article proposes a judicial judgment prediction method CGLJ based on causal graph analysis.Firstly

the causal relationships among elements are mined from unstructured legal fact description texts.Then a causal graph is constructed using a composition method of easily confused accusation clustering.It not only considers the difference among similar fact descriptions

but also enhances the interaction between fact descriptions and laws and regulations.Finally

the constructed causality diagram is integrated into a deep neural network for joint inference to obtain the decision prediction result.In addition

this paper also visualizes the causal diagram inference process in the model prediction

providing better interpretability for the judgment result.The experimental result on the CAIL2018 judicial judgment prediction dataset shows that the proposed method achieves better result than the baseline models.

关键词

Keywords

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