发表机构
University of Bath（巴斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过构建序贯政治经济模型，分析规模化AI问责的制度问题，揭示代理合规均衡的形成机制，指出独立审计权、可移植性等因素对执法与缓解的影响，解释了形式合规与运营结果存在差距的原因。
AI 中文摘要
规模化AI问责是一个制度问题：谁能观测、验证并修改已部署的系统。我们构建了一个序贯政治经济模型，其中AI供应商选择可审计性与实质性缓解措施，部署方在采用后面临转换成本时进行监测，而执法取决于可验证证据。鉴于部署方的监测反应，供应商可能止步于可观测的采购基准，同时在低于社会最优水平的情况下进行缓解，从而形成代理合规均衡。我们刻画了唯一的内部均衡与危害被完全缓解的角点均衡。独立审计权直接提高执法曝光度；可移植性恢复部署方的议价能力；事件报告增加了监管机构可见的证据渠道；结果关联责任创造了不依赖供应商控制的检测的激励。这些结果解释了为何文档与标准化评估能与持续的部署后危害并存，并为监测、缓解以及形式合规与运营结果之间的差距提供了可检验的含义。
英文摘要
AI accountability at scale is an institutional problem: who can observe, verify, and change deployed systems. We develop a sequential political-economy model in which an AI vendor chooses auditability and substantive mitigation, a deployer monitors after adoption while facing switching costs, and enforcement depends on verifiable evidence. Anticipating the deployer's monitoring response, the vendor may stop at an observable procurement floor while mitigating below the social first best, producing a proxy-compliance equilibrium. We characterize the unique interior equilibrium and the corner in which harm is fully mitigated. Independent audit rights raise enforcement exposure directly; portability restores deployer leverage; incident reporting adds a regulator-visible evidence channel; and outcome-linked liability creates incentives that do not depend on vendor-controlled detection. The results explain why documentation and standardized evaluations can coexist with persistent post-deployment harms, and generate testable implications for monitoring, mitigation, and the gap between formal compliance and operational outcomes.
CommentsAccepted at AAAI/ACM Conference on AI, Ethics, and Society (AIES '26)
