实证法律分析简化:通过自动识别和评估具有法律意义的因素来减少复杂性
Morgan A Gray1, Jaromir Savelka2, Wesley M Oliver3
1Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA.
概括
这项研究引入了一种新的人工智能辅助注释方法,以加快法律研究. 使用GPT模型,它减少了对AI和法律以及实证法律研究的判例分析的负担.
科学领域:
- 法律信息学 法律信息学
- 计算社会科学 计算社会科学
- 人工智能在法律中的作用
背景情况:
- 实证法律研究和人工智能和法律研究通常依赖于手动案例文本注释.
- 手动注释是耗时的,昂贵的,并可能影响数据质量.
- 简化注释对于推进这些领域至关重要.
研究的目的:
- 研究一种新的注释方法,使用GPT家庭模型来帮助人类注释者.
- 为了减少与AI和法律和实证法律研究中注释相关的复杂性,成本和时间.
- 提高法律研究项目注释的速度,成本和质量.
主要方法:
- 开发了一种利用 GPT 家庭模型的新注释方法.
- 使用了与警方根据贩毒嫌疑逮捕驾驶员的权力相关的案件和因素.
- 应用文本分析的最新进展,用于因素识别.
主要成果:
- 在大型案件中显著降低了识别因素的负担.
- 在机器学习模型对病例结果的预测方面取得了改进.
- 在特定的法律背景下验证方法的有效性.
结论:
- 拟议的GPT辅助注释方法有效地减少了法律研究中的注释负担.
- 文本分析的进步可以提高实证法律研究和人工智能和法律的效率和准确性.
- 这种方法有可能大大有利于人工智能和法律和实证法律研究项目.
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