构建可交互的参与式AI地图，助力政策与社区监督。
Voices in the Loop: Mapping Participatory AI
- 建立可复现的参与式AI记录采集与审核流程。
- 发现参与主要集中在问题定义、评估与治理环节。
- 提供版本化更新与争议反馈机制，支持持续迭代。
参与式人工智能在公共、公民及人道主义场景中日益受到关注，但其组织方式的证据仍零散。本文基于Magaña和Shilton的可信AI语料库，结合研究与实践中的审计案例，构建了一个开放的参与式AI倡议资源库与互动地图。贡献包括：第一，提出一套可复现的记录发现、审核、标准化、地理编码、溯源追踪及发布出版协议；第二，报告了地理分布、参与层级、生命周期节点、组织形式、验证状态及文档缺口等整体模式——当前记录集中于少数国家，参与多见于问题定义、评估与治理阶段，模型开发与训练阶段参与度较低；第三，通过版本化发布、记录关联的问题与注释通道、模式反馈流程以及数据脱敏或受限披露请求，实现参与式默认的AI基础设施设计与治理框架。该地图旨在通过动态可更新、可争辩、可重用的库存，支持比较研究、政策学习与社区监督。
原文摘要 · Abstract (English)
Participatory approaches to artificial intelligence are increasingly documented across public, civic, and humanitarian settings, but evidence about how participation is organized remains fragmented. This paper reports on the construction of an open repository and interactive atlas of participatory AI initiatives, using records harmonized from Maga~na and Shilton's Trustworthy AI corpus, and additional audited cases from research and practice. We contribute three elements. First, we specify a reproducible protocol for discovery, vetting, harmonization, geocoding, provenance tracking, and release-based publication of participatory AI records. Second, we report corpus-level patterns in geography, participation tiers, lifecycle loci, organizational form, verification status, and remaining documentation gaps. Documented initiatives remain concentrated in a small number of countries, while participation is most often coded at problem formulation, evaluation, and governance rather than model development or training. Third, we show how the atlas operationalizes a design and governance framework for participatory-by-default AI infrastructures through versioned releases, record-linked issue and annotation channels, schema feedback workflows, and redaction or restricted-disclosure requests. The atlas is intended to support comparative research, policy learning, and community scrutiny through a living inventory that can be updated, contested, and reused.
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