法律逻辑模型用于预测印度法院的司法分歧
Sivaranjani N1, Jayabharathy J2
1School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology (VIT) Chennai Campus, Chennai, Tamil Nadu, India.
Frontiers in artificial intelligence
|November 7, 2025
概括
新的法律逻辑模型 (LLM) 通过分析案件和法官因素,准确预测最高法院的决定. 这种可解释的AI框架达到80%的准确性,在预测司法分歧方面表现优于现有的模型.
科学领域:
- 计算社会科学 计算社会科学
- 法律分析 法律分析
- 人工智能在法律中的作用
背景情况:
- 预测最高法院的结果对于理解司法决策至关重要.
- 现有的模型努力平衡预测准确性与可解释性.
研究的目的:
- 介绍法律逻辑模型 (LLM),一种基于神经网络的新方法.
- 提高最高法院撤销的预测能力,并确定关键影响因素.
- 将离散选择理论的解释性与神经网络的灵活性相结合.
主要方法:
- 开发了一个进化的神经网络模型,法律逻辑模型 (LLM).
- 从法律案件和司法个人资料中整合特征.
- 在最高法院意见的数据集上培训和测试了LLM.
主要成果:
- 该LLM在预测最高法院案件结果方面取得了80%的准确性.
- 法律法学超越了传统的逻辑和深度学习模型.
- 确定了影响司法分歧的重大案件和法官特定因素.
结论:
- 该LLM提供了一个可解释和有效的框架,用于预测司法决定.
- 这种模型通过提供透明度来推进司法决策的研究.
- 该LLM展示了混合AI方法在法律分析中的潜力.
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