arXiv:2604.20711v2 Announce Type: replace Abstract: Artificial intelligence is increasingly deployed to synthesize large-scale public input in policy consultations and participatory processes. Yet no formal framework exists for auditing whether these summaries faithfully represent the source population, an accountability gap that existing approaches to AI explainability, grounding and hallucination detection do not address because they focus on output quality rather than input fidelity. Here, participatory provenance is introduced: a measurement framework grounded in optimal transport theory,
As AI models advance in their ability to synthesize complex public input, the need for robust accountability frameworks for democratic processes becomes critical, driving the development of new auditing methodologies.
This development addresses a critical gap in AI governance by proposing a formal method to ensure AI summaries of public consultation faithfully represent their source populations, which is vital for maintaining public trust and democratic integrity.
The introduction of 'participatory provenance' provides a specific, theory-grounded framework for auditing AI's performance in public consultation, shifting the focus from mere output quality to input fidelity in AI applications within governance.
- · Governments utilizing AI for public consultation
- · Civic technology developers
- · Public policy researchers
- · Citizens participating in AI-mediated consultations
- · AI developers ignoring input fidelity
- · Entities seeking to manipulate public opinion through opaque AI processes
- · Traditional, less rigorous auditing methods
Increased trust in AI-mediated public consultation processes due to enhanced auditability.
Demand for AI systems that inherently incorporate 'participatory provenance' principles in their design, leading to new development standards.
Potential for 'misinformation auditing' to evolve, applying similar concepts of provenance and representational fidelity to broader content analysis beyond policy consultations.
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Read at arXiv cs.AI