Abstract
Artificial intelligence researchers have made significant advances in legal intelligence in recent years. However, the existing studies have not focused on the important value embedded in judgments reversals, which limits the improvement of the efficiency of legal intelligence. In this paper, we propose a causal Framework for Accurately Inferring case Reversals (FAIR), which models the problem of judgments reversals based on real Chinese judgments. We mine the causes of judgments reversals by causal inference methods and inject the obtained causal relationships into the neural network as a priori knowledge. And then, our framework is validated on a challenging dataset as a legal judgment prediction task. The experimental results show that our framework can tap the most critical factors in judgments reversal, and the obtained causal relationships can effectively improve the neural network’s performance. In addition, we discuss the generalization ability of large language models for legal intelligence tasks using ChatGPT as an example. Our experiment has found that the generalization ability of large language models still has defects, and mining causal relationships can effectively improve the accuracy and explain ability of model predictions.
M. He and N. Gu—Contribute equally to this work.
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We utilize a dataset sample sourced from publicly available judgment documents on the China Judgment Network, which is a platform that complies with relevant legal and regulatory requirements and authorizes the use of documents for research purposes. Our objective is to support legal services through FAIR principles and aid judges in their decision-making process rather than replace them. However, crucial information pertaining to over-age labors is often absent or ambiguous due to privacy concerns. This can result in the dataset being incomplete, potentially impacting the final analysis results. In certain cases, our model may generate erroneous judgments; hence users must exercise caution when interpreting the model’s inference results. Nevertheless, on the whole, our model can assist judges in identifying pertinent legal articles and aid in ensuring judicial consistency throughout China.
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He, M., Gu, N., Shi, Y., Zhang, Q., Chen, Y. (2023). FAIR: A Causal Framework for Accurately Inferring Judgments Reversals. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14254. Springer, Cham. https://doi.org/10.1007/978-3-031-44207-0_15
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