呈递给法庭：人工智能在美国联邦法院判决中的诉讼情况（及未涉及情况）
Visible to the Court: How AI Is (and Isn't) Litigated in U.S. Federal Court Opinions
浏览论文内容
中文总结 AI 辅助
研究美国联邦法院中人工智能诉讼情况，通过回顾559份判决进行分类，识别争议领域、技术及当事人等，发现与数据库有差距，法院靠现有原则治理，导致部分危害未解决。
中文摘要 AI 辅助
在美国，人工智能在联邦监管有限的情况下迅速部署。随着法院成为审查人工智能相关实践的常设场所，通过实证了解目前的人工智能诉讼情况很重要。我们通过系统回顾559份美国联邦法院判决来填补这一空白，这些判决中人工智能在当事人的争议中发挥作用，对争议的常见主题、涉及的人工智能技术以及相关当事人进行分类。我们识别出七个反复出现的争议领域、诉讼核心的六类人工智能技术、四类常见诉讼当事人以及当事人使用的法律原则。与人工智能事件数据库的比较显示，记录的和诉讼中的危害在覆盖范围、定义和普遍性方面存在重大差距，表明法院仅捕捉到了部分人工智能风险情况。此外，我们发现法院判决主要依靠现有法律原则来管理人工智能，而非制定新的特定于人工智能的法律，产生了一种“零碎”的人工智能治理形式。结果，联邦法院的判决结果更多地取决于现有法规可认知的危害，而非人工智能造成危害的地点，导致某些人工智能危害仍未得到解决。
英文摘要
In the United States, artificial intelligence (AI) is rapidly deployed amid limited federal regulation. With courts become a recurring forum in which AI-related practices are scrutinized, it is important to empirically understand the AI litigation landscape to date. We address this gap through a systematic review of 559 U.S. federal court opinions in which AI plays a role in the parties' contentions, taxonomizing (1) common topics of dispute, (2) the AI technologies implicated, and (3) the parties involved, including common plaintiff and defendant types. We identify seven recurring dispute areas, six categories of AI technologies at the center of litigation, and four types of common litigants, alongside legal doctrines used by the litigants. A comparison of this taxonomy to the AI Incident Database revealed substantial gaps in coverage, definitions, and prevalence between documented and litigated harms, suggesting courts capture only part of the AI risk landscape. In addition, we found that court decisions primarily rely on pre-existing legal doctrines to manage AI rather than making new AI-specific laws, producing a form of "piecemeal" AI governance. As a result, federal court outcomes are shaped less by where AI has caused harms and more by which harms are cognizable under existing statutes, leading to certain AI harms remaining unresolved.
发表机构
- University of Washington（华盛顿大学）
机构由 AI 辅助整理，请以论文原文为准。
