Participatory approaches are widely invoked in AI governance, yet participation rarely translates into durable influence. In public sector and civic AI systems, community contributions such as deliberations, annotations, prompts, and incident reports are often recorded informally, weakly linked to system updates, and disconnected from enforceable rights or sustained compensation. As a result, participation is frequently symbolic rather than accountable. We introduce the Participation Ledger, a machine readable and auditable framework that operationalizes participation as traceable influence, enforceable authority, and compensable labor. The ledger represents participation as an influence graph that links contributed artifacts to verified changes in AI systems, including datasets, prompts, adapters, policies, guardrails, and evaluation suites. It integrates three elements: a Participation Evidence Standard documenting consent, privacy, compensation, and reuse terms; an influence tracing mechanism that connects system updates to replayable before and after tests, enabling longitudinal monitoring of commitments; and encoded rights and incentives. Capability Vouchers allow authorized community stewards to request or constrain specific system capabilities within defined boundaries, while Participation Credits support ongoing recognition and compensation when contributed tests continue to provide value. We ground the framework in four urban AI and public space governance deployments and provide a machine readable schema, templates, and an evaluation plan for assessing traceability, enforceability, and compensation in practice.

翻译：参与式方法在AI治理中被广泛援引，但参与行为很少能转化为持久的影响力。在公共部门与公民AI系统中，诸如审议、标注、提示词和事件报告等社区贡献往往被非正式地记录，与系统更新的关联薄弱，且与可执行的权利或持续补偿脱节。因此，参与常常流于形式而非具备问责效力。本文提出“参与账本”，这是一个机器可读且可审计的框架，它将参与操作化为可追溯的影响力、可执行的权威以及可补偿的劳动。该账本将参与表示为一种影响力图谱，将贡献的产出物（包括数据集、提示词、适配器、策略、防护栏和评估套件）与AI系统中经过验证的变更相链接。它整合了三个要素：记录同意、隐私、补偿及重用条款的《参与证据标准》；将系统更新与可复现的前后测试相连接的影响追踪机制，从而实现对承诺的纵向监测；以及经编码的权利与激励机制。能力凭证允许经授权的社区管理者在既定范围内请求或限制特定的系统能力，而参与积分则用于在贡献的测试持续提供价值时提供持续的认可与补偿。我们将该框架基于四个城市AI与公共空间治理的实际部署进行论证，并提供了机器可读的模式、模板以及评估计划，用以在实践中评估可追溯性、可执行性与补偿机制。