Traceable, Enforceable, and Compensable Participation: A Participation Ledger for People-Centered AI Governance

TL;DR

This paper introduces the Participation Ledger, a framework that makes AI governance participation traceable, enforceable, and compensable by linking community contributions to system updates and rights management.

Contribution

It presents a novel, machine-readable ledger framework that operationalizes accountable participation with influence tracking, enforceable rights, and compensation mechanisms in AI governance.

Findings

Demonstrated deployment in four urban AI governance projects.

Developed a schema, templates, and evaluation plan for practical implementation.

Enabled longitudinal monitoring of participation influence and system commitments.

Abstract

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.