<aside> âšī¸ This presentation at PAIRS 2026 Online on 17th February 2026 16:00 UTC. Registered participants will receive zoom links to join the session via e-mail.
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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 typically recorded informally, weakly linked to system updates, and disconnected from enforceable rights or sustained compensation. As a result, participation is often 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. The design integrates three core elements. First, a Participation Evidence Standard records who participated, under what consent, privacy, and compensation terms, how representational gaps were addressed, and how contributions may be reused. Second, an influence tracing mechanism ties system updates to replayable before and after tests, preserving provenance from community raised concerns to implemented changes. These tests can be re executed in future releases, forming a participatory evaluation harness that detects regressions and erosion of commitments over time. Third, the ledger encodes rights and incentives. Capability Vouchers allow authorized community stewards to request, constrain, or pause specific system capabilities within defined adoption boundaries, while Participation Credits support ongoing recognition and compensation when contributed tests continue to provide value. We ground the ledger in four deployed cases in urban AI and public space governance, spanning participatory datasets, civic planning tools, evaluation frameworks, and generative design platforms. Across cases, we identify recurring documentation and accountability gaps and show how the ledger makes participation legible, enforceable, and durable. We contribute a machine readable schema, reference templates, and an evaluation plan for assessing traceability, enforceability, and compensation in real world deployments.