发表机构
The Information School, University of Wisconsin–Madison（威斯康星大学麦迪逊分校信息学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出可读性陷阱概念，解释公共部门参与式AI治理中参与差距的持续存在，并通过三个案例的批判性话语分析揭示记录机制如何过滤约束国家权力的社区意见。
AI 中文摘要
公共部门机构已采用参与式设计作为针对算法伤害提出的重要程序性保障措施。然而，在不同方法、制度背景和善意实施中，参与差距持续存在。社区参与，意见被记录，系统部署后基本不变。本文将该差距的持续性归因于机构用于记录参与本身的机制。我们提出了可读性陷阱的概念，即机构记录参与的机制系统性地选择符合机构类别的社区意见形式，并过滤掉最可能约束国家权力的形式，包括根本性异议、要求不部署的诉求，以及凯利·奥利弗所称的对结构性伤害条件的见证。借鉴詹姆斯·斯科特的国家简化理论，我们通过对三个公共部门人工智能部署中的二十二份官方、社区制作和公共记录文件进行批判性话语分析来展示可读性陷阱：阿勒格尼家庭筛查工具、底特律项目绿灯以及奥克兰的社区对警察监控控制条例。我们识别了参与部分逃脱陷阱的结构性条件，并理论化认为反复不回应构成对社区主体性的伤害，而人工智能伦理框架尚未对此予以命名。
英文摘要
Public sector agencies have adopted participatory design as a prominent procedural safeguard proposed against algorithmic harm. Across methods, institutional contexts, and well-intentioned implementations, the participation gap persists. Communities engage, input is documented, and systems are deployed largely unchanged. This paper locates the persistence of that gap in the mechanism institutions use to document participation itself. We develop the concept of the legibility trap, in which institutional mechanisms for documenting participation systematically select for forms of community input that fit institutional categories and filter out the forms that would most constrain state power, including foundational dissent, demands for non-deployment, and what Kelly Oliver calls witnessing to structural conditions of harm. Drawing on James Scott's theory of state simplification, we demonstrate the legibility trap through critical discourse analysis of twenty-two official, community-produced, and public-record documents across three public sector AI deployments: the Allegheny Family Screening Tool, Detroit Project Green Light, and the Community Control Over Police Surveillance (CCOPS) ordinances in Oakland. We identify the structural conditions under which participation partially escapes the trap, and we theorize that repeated non-response constitutes a harm to community subjectivity that AI ethics frameworks have not yet named.
