Traceable, Enforceable, and Compensable Participation: A Participation Ledger for People-Centered AI Governance
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
FedPoP: Federated Learning Meets Proof of Participation
Federated learning (FL) offers privacy preserving, distributed machine learning, allowing clients to contribute to a global model without revealing their local data. As models increasingly serve as monetizable digital as…
Federated LearningCasual Social Media Use among the Youth: Effects on Online and Offline Political Participation
Background: Previous studies suggest that social media use among the youth is correlated with online and offline political participation. There is also a mixed and inconclusive debate on whether more online political par…
Bringing AI Participation Down to Scale: A Comment on Open AIs Democratic Inputs to AI Project
In 2023, Open AIs Democratic Inputs program funded 10 teams to design procedures for public participation in generative AI. In this Perspective, we review the results of the project, drawing on interviews with some of th…
Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation
Existing works in federated learning (FL) often assume an ideal system with either full client or uniformly distributed client participation. However, in practice, it has been observed that some clients may never partici…
Federated LearningCan Machine Learning discover the determining factors in participation in insurance schemes? A comparative analysis
Identifying factors that affect participation is key to a successful insurance scheme. This study's challenges involve using many factors that could affect insurance participation to make a better forecast.Huge numbers o…
Variable Selection