Participatory provenance as representational auditing for AI-mediated public consultation
AI-assisted consultation can speed large-scale public engagement, but concise summaries may reflect some submissions more closely than others. This paper introduces participatory provenance, a framework for auditing how semantic coverage is distributed from submissions to summary sentences. Applied to two topics in Canada's 2025 AI Strategy consultation (5,253 records; 2,861 participants), official summaries had higher observed mean coverage than exact-length random text, although statistical significance depended on the embedding model. Low coverage concentrated in semantic regions, especially those centered on criticism of educational technology and distrust of technology and oversight,...
| Authors | Sachit Mahajan |
|---|---|
| Primary category | cs.AI |
| Published | 22 April 2026 |
| Last updated at source | 20 July 2026 |
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