Causal Artificial Intelligence in Legal Language Processing: A Systematic Review.

IF 2.1 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Entropy Pub Date : 2025-03-28 DOI:10.3390/e27040351

Philippe Prince Tritto, Hiram Ponce

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引用次数: 0

Abstract

Recent advances in legal language processing have highlighted limitations in correlation-based artificial intelligence approaches, prompting exploration of Causal Artificial Intelligence (AI) techniques for improved legal reasoning. This systematic review examines the challenges, limitations, and potential impact of Causal AI in legal language processing compared to traditional correlation-based methods. Following the Joanna Briggs Institute methodology, we analyzed 47 papers from 2017 to 2024 across academic databases, private sector publications, and policy documents, evaluating their contributions through a rigorous scoring framework assessing Causal AI implementation, legal relevance, interpretation capabilities, and methodological quality. Our findings reveal that while Causal AI frameworks demonstrate superior capability in capturing legal reasoning compared to correlation-based methods, significant challenges remain in handling legal uncertainty, computational scalability, and potential algorithmic bias. The scarcity of comprehensive real-world implementations and overemphasis on transformer architectures without causal reasoning capabilities represent critical gaps in current research. Future development requires balanced integration of AI innovation with law's narrative functions, particularly focusing on scalable architectures for maintaining causal coherence while preserving interpretability in legal analysis.

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法律语言处理中的因果人工智能：系统综述。

法律语言处理的最新进展突出了基于关联的人工智能方法的局限性，促使人们探索因果人工智能（AI）技术来改进法律推理。与传统的基于关联的方法相比，本系统综述探讨了因果人工智能在法律语言处理中的挑战、局限性和潜在影响。根据乔安娜布里格斯研究所的方法，我们分析了2017年至2024年学术数据库、私营部门出版物和政策文件中的47篇论文，通过严格的评分框架评估了因果人工智能的实施、法律相关性、解释能力和方法质量，评估了它们的贡献。我们的研究结果表明，与基于关联的方法相比，因果人工智能框架在获取法律推理方面表现出优越的能力，但在处理法律不确定性、计算可扩展性和潜在的算法偏差方面仍存在重大挑战。缺乏全面的现实世界实现和过分强调没有因果推理能力的变压器架构代表了当前研究的关键空白。未来的发展需要人工智能创新与法律叙事功能的平衡整合，特别是关注可扩展的架构，以保持因果一致性，同时保持法律分析的可解释性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Entropy PHYSICS, MULTIDISCIPLINARY-

CiteScore

4.90

自引率

11.10%

发文量

1580

审稿时长

21.05 days

期刊介绍： Entropy (ISSN 1099-4300), an international and interdisciplinary journal of entropy and information studies, publishes reviews, regular research papers and short notes. Our aim is to encourage scientists to publish as much as possible their theoretical and experimental details. There is no restriction on the length of the papers. If there are computation and the experiment, the details must be provided so that the results can be reproduced.