法律语言处理中的因果人工智能:系统性审查
Philippe Prince Tritto1, Hiram Ponce2
1Facultad de Derecho, Universidad Panamericana, Augusto Rodin 498, Mexico City 03920, Mexico.
Entropy (Basel, Switzerland)
|April 26, 2025
概括
因果人工智能 (AI) 显示出法律推理对基于关联的AI的承诺. 然而,必须解决法律不确定性,可扩展性和偏见等挑战,以便在法律语言处理中有效实施.
科学领域:
- 人工智能的人工智能
- 法律技术 法律技术
- 计算语言学 计算语言学
背景情况:
- 在法律语言处理中,传统的基于关联的AI方法存在局限性.
- 人工智能 (AI) 为增强法律推理提供了一个潜在的替代方案.
- 人工智能在法律中的整合需要探索先进的因果推理技术.
研究的目的:
- 系统地审查和分析因果AI在法律语言处理中的挑战,局限性和影响.
- 在法律背景下,比较因果AI与基于关联的AI方法的有效性.
- 识别法律应用中因果AI的研究缺口和未来方向.
主要方法:
- 按照乔安娜·布里格斯研究所的方法进行系统的文献综述.
- 来自学术界,私营部门和政策来源的47篇论文 (2017-2024) 的分析.
- 严格的评分框架评估因果AI实施,法律相关性,可解释性和方法质量.
主要成果:
- 与基于关联的方法相比,因果AI框架在捕捉法律推理方面表现出更好的能力.
- 重要挑战包括处理法律不确定性,计算可扩展性和算法偏差.
- 目前的研究表明,现实世界的实现很少,并且过度依赖于非因果变压器架构.
结论:
- 因果AI在推进法律推理和语言处理方面具有重大潜力.
- 解决不确定性,可扩展性和偏见方面的挑战对于实际采用至关重要.
- 未来的研究应该专注于将人工智能创新与法律的叙事功能相结合,强调可扩展和可解释的因果架构.
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