作为陪审员的大型语言模型 (LLM):评估LLM在法律环境中的潜力
Yongjie Sun1, Angelo Zappalà2, Eleonora Di Maso3
1Department of Social Sciences, Faculty of Arts and Sciences, New York University Shanghai.
Law and human behavior
|October 20, 2025
概括
大型语言模型 (LLM) 在模拟陪审团实验中显示了更高的有罪概率,但比人类有罪的判决更少. 虽然一致,但LLM表现出偏见,特别是在报告延迟和种族因素方面.
科学领域:
- 法律心理学 法律心理学
- 人工智能的人工智能
- 计算社会科学 计算社会科学
背景情况:
- 模拟陪审团实验传统上评估人类决策偏见.
- 大型语言模型 (LLM) 越来越能够完成复杂的认知任务.
- 在法律决策中应用LLM需要仔细检查其潜在的偏见.
研究的目的:
- 评价法学士 (GPT-4o,克劳德3.5索内特,GPT-o1) 作为模拟陪审员的表现.
- 调查LLM对影响人类陪审员偏见的因素的反应,包括被告的种族,社会地位,指控号码和报告延迟.
- 将LLM决策一致性和偏见与人类陪审员基准进行比较.
主要方法:
- 复制的弗雷泽等人. (2023) 模拟陪审团实验使用高级法学士.
- 一个2x2x2x3的因数设计,操纵被告的种族,社会地位,指控数量和报告延迟.
- 收集了2,304个LLM答案,评估了有罪的概率,判决和调解/调节变量.
主要成果:
- 与人类相比,LLM的平均内概率 (63.56%) 较高,但对有罪的判决较少 (21%).
- 法律律师对白人被告有偏见,并增加了多项指控的内感,反映了一些人类的偏见.
- 与人类陪审员不同的是,LLM对报告延迟很敏感,在较短的延迟中分配更高的内,而不是人类陪审员.
结论:
- 与人类相比,LLM在法律决策中提供了更大的一致性.
- 法律法师并非免受偏见的影响,并且可以独特地解释法律案例因素.
- 需要进一步的研究来理解和减轻AI法律决策中的潜在偏见.
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