[论文解读] Man and machine: artificial intelligence and judicial decision making

Arthur Dyevre, Ahmad Shahvaroughi|arXiv (Cornell University)|Mar 19, 2026

Ethics and Social Impacts of AI被引用 0

一句话总结

本论文综合研究了司法决策中的人工智能应用，聚焦风险评估工具、法官人类偏见以及法官如何与AI建议互动，发现AI辅助的影响有限或无显著影响，但存在显著的证据缺口。

ABSTRACT

The integration of artificial intelligence (AI) technologies into judicial decision-making, particularly in pretrial, sentencing, and parole contexts, has generated substantial concerns about transparency, reliability, and accountability. At the same time, these developments have brought the limitations of human judgment into sharper relief and underscored the importance of understanding how judges interact with AI-based decision aids. Using criminal justice risk assessment as a focal case, we conduct a synthetic review connecting three intertwined aspects of AI's role in judicial decision-making: the performance and fairness of AI tools, the strengths and biases of human judges, and the nature of AI-plus-human interactions. Across the fields of computer science, economics, law, criminology, and psychology, researchers have made significant progress in evaluating the predictive validity of automated risk assessment instruments, documenting biases in judicial decision-making, and, to a more limited extent, examining how judges use algorithmic recommendations. While the existing empirical evidence indicates that the impact of AI decision-aid tools on pretrial and sentencing decisions is modest or nonexistent, our review also reveals important gaps in the existing literature. Further research is needed to evaluate the performance of AI risk assessment instruments, understand how judges navigate uncertain decision-making environments, and examine how individual characteristics influence judges' responses to AI advice. We argue that AI-versus-human comparisons have the potential to yield new insights into both algorithmic tools and human decision-makers. We advocate greater interdisciplinary integration to foster cross-fertilization in future research.

研究动机与目标

• 理解AI工具在审前、量刑与假释决策中的使用情况以提高理解。
• 综合 Automated 风险评估工具的预测有效性与公平性的证据。
• 检视人类法官的偏见及其与算法推荐的互动。
• 识别文献中的空白并为跨学科研究提出方向。

提出的方法

• 跨计算机科学、经济学、法律、犯罪学与心理学领域的研究发现的汇总。
• 将AI工具的表现与公平性证据与法官的决策偏差联系起来。
• 评估在有AI建议时法官如何在不确定的决策环境中导航。
• 讨论AI对照人类比较在带来新见解方面的潜力。

实验结果

研究问题

• RQ1 自动化风险评估工具在刑事司法中的预测有效性如何？
• RQ2 人类法官的偏见如何与基于AI的决策辅助工具互动？
• RQ3 AI决策辅助工具是否对审前与量刑结果产生实质性影响？
• RQ4 关于司法情境中的AI–人类互动，文献中存在哪些空白？
• RQ5 跨学科整合如何提升对司法决策中AI的理解？

主要发现

• 证据表明AI决策辅助工具对审前与量刑决策的影响要么有限、要么不存在。
• 在评估AI风险评估工具的性能以及法官对AI建议的互动方面存在重要空白。
• 现有文献在衡量工具公平性和预测有效性方面取得进展，但对实际决策结果的研究仍然有限。
• 法官在不确定决策环境中的导航以及个体特征如何影响对AI建议的反应需要进一步研究。
• AI对照人类的比较有潜力为工具与决策者带来新的见解。
• 论文主张加强跨学科整合，以促进未来研究中的相互促进。