研究AI责任机制如何在大规模部署中失效，揭示合规与实际效果的差距。
Scaling, Lock-In, and Proxy Compliance: A Political Economy of Responsible AI
- 构建多阶段政治经济学模型，分析厂商、部署方与监管者互动
- 发现厂商仅满足最低可审计标准，导致损害未完全缓解
- 提出审计权、数据可迁移等机制可提升真实问责效果
AI责任在规模化部署下本质上是制度问题：谁能够观察、验证并改变已部署系统。本文构建了一个序列化的政治经济学模型，其中AI供应商决定系统的可审计性与实质性缓解措施，部署方在采纳后面临转换成本而进行监控，监管依赖可验证证据。考虑到部署方的监控反应，供应商可能仅满足可观测的采购底线，而在实质性缓解上低于社会最优水平，形成代理合规均衡。我们刻画了唯一内部均衡以及损害被完全缓解的边界情况。独立审计权直接提高执法暴露度；数据可移植性恢复部署方议价能力；事件报告为监管提供可见证据渠道；后果关联的责任机制则创造不依赖供应商检测的激励。研究解释了为何文档和标准化评估能与持续的部署后危害共存，并为监控、缓解及形式合规与实际结果之间的差距提供了可检验推论。
原文摘要 · Abstract (English)
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.
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