AI 中文总结
本文研究美国三种联邦AI透明度机制的不足，提出三角测量法关联其披露内容，通过案例研究指出关联记录仍不足以监督，针对AI用例清单提出三项结构性改进建议。
AI 中文摘要
联邦AI系统可能会拒绝提供福利或标记个人以进行驱逐，但旨在使这些系统可见的公开披露是碎片化的，详细程度参差不齐。本文研究了美国现有的三种联邦透明度机制——记录系统通知（SORNs）、信息收集请求（ICRs）和AI用例清单，并探讨了它们单独及共同描述政府AI使用情况的程度。我们发现，没有任何一种机制能完全揭示政府如何构建或部署AI：每种机制披露系统的不同方面，当前的披露基础设施使公众很难跨监管机制和时间追踪特定AI系统。系统缺乏持久标识符，粒度差异很大，而AI用例清单的年度周期意味着联邦机构可以在任何官方记录中出现前数月就部署系统。我们使用经人工验证的零样本分类和跨文档实体解析，提出了一种三角测量方法，将三种机制的披露内容关联起来，并提供了两个案例研究。我们的案例研究发现，关联记录能更深入地了解政府AI使用情况，但与公开报道中关于相同系统的信息相比，即使是关联记录也不足以进行监督。我们将每种机制的披露缺陷追溯到其最初的行政目的，表明这些缺口是结构性的，并提出了以AI用例清单为最适合面向公众的透明度机制的建议：（1）广泛且一致适用的AI系统定义；（2）带有相关披露交叉引用的持久系统标识符；（3）恢复对风险管理流程的公众可见性。
英文摘要
Federal AI systems can deny benefits or flag individuals for deportation, but the public disclosures meant to make those systems visible are fragmented and unevenly detailed. This paper examines three existing U.S. federal transparency regimes---System of Records Notices (SORNs), Information Collection Requests (ICRs), and the AI Use Case Inventory---and asks how well they, individually and together, describe government AI use. We find that no single regime fully reveals how the government constructs or deploys AI: each discloses different aspects of a system, and the current disclosure infrastructure makes it very challenging for the public to track specific AI systems across regulatory regimes and over time. Persistent identifiers are absent, granularity varies widely, and the annual AI Use Case Inventory cycle means federal agencies can deploy systems months before appearing in any official record. Using hand-validated zero-shot classification and cross-document entity resolution, we contribute a triangulation method that links disclosures across all three regimes and present two case studies. Our case studies finds that linking records provides greater insight into government AI use, but even linked records would constitute insufficient oversight compared to what public reporting has revealed about the same systems. We trace each regime's disclosure weaknesses to its original administrative purpose, showing these gaps are structural, and offer recommendations focused on the AI Use Case Inventory as the mechanism best suited for public-facing transparency: (1) a broad and consistently applied AI system definition, (2) persistent system identifiers with cross-references to related disclosures, and (3) restored public visibility into risk management processes.
CommentsAIES 2026
