基于因果图分析的可解释司法判决预测方法研究

doi:10.11959/j.issn.2096-0271.2024023

Abstract

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.

Key words: legal judgment prediction, causal diagram, interpretability

CLC Number:

TP18

Hu ZHANG, Zhen ZHANG, Yue FAN, Jiayu GUO. Research on interpretable legal judgment prediction method based on causal graph analysis[J]. Big Data Research, 2024, 10(2): 109-121.

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数据集	训练集样本数	测试集样本数	法条数	罪名数
CAIL-big	1 587 979	185 120	118	130
CAIL-deal	1 395 496	164 173	67	85

模型	法条预测
模型	1%1%	10%10%	30%30%	50%50%	100%100%
LSTM	0.34	0.41	0.51	0.66	0.79
CNN	0.32	0.43	0.57	0.74	0.83
BERT+CNN	0.35	0.48	0.62	0.80	0.89
LSTM+Casual	0.51	0.57	0.64	0.75	0.87
CNN+Casual	0.54	0.61	0.73	0.87	0.91
CGFJ(BERT+CNN+Casual)	0.55	0.62	0.75	0.89	0.937

模型	罪名指控预测
模型	1%	10%	30%	50%	100%
LSTM	0.29	0.34	0.43	0.54	0.69
CNN	0.31	0.37	0.48	0.62	0.79
BERT+CNN	0.34	0.41	0.56	0.69	0.84
LSTM+Casual	0.46	0.51	0.57	0.64	0.76
CNN+Casual	0.47	0.54	0.61	0.71	0.83
CGFJ(BERT+CNN+Casual)	0.51	0.56	0.65	0.77	0.87

Research on interpretable legal judgment prediction method based on causal graph analysis

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