Voices in the Loop: Mapping Participatory AI

2026-05-16

AI Summary

Participatory artificial intelligence is increasingly deployed in public and humanitarian domains, yet empirical evidence remains fragmented and lacks systematic synthesis. This study proposes the first default infrastructure framework for participatory AI, featuring versioned releases, annotation linkage, feedback mechanisms, and sensitive information handling. Built upon reproducible protocols, the framework establishes an open, auditable, and scalable knowledge base and interactive atlas through data harmonization, geocoding, provenance tracking, and structured metadata modeling. The research reveals that global initiatives are highly concentrated in a few countries and predominantly focus on problem formulation, evaluation, and governance phases. The resulting resources provide a dynamic foundation for comparative analysis, policy learning, and public accountability.

Abstract

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.

Problem

• participatory AI
• evidence fragmentation
• AI governance
• public participation
• trustworthy AI

Innovation

• Participatory AI
• open repository
• interactive atlas
• reproducible protocol
• participatory governance