Participatory provenance as representational auditing for AI-mediated public consultation

2026-04-22

AI Summary

This study addresses the lack of auditable mechanisms for ensuring representativeness of public input in AI-generated policy consultation summaries, which often systematically exclude marginalized or dissenting voices. The authors propose the first participatory provenance framework integrating optimal transport theory, causal inference, and semantic analysis to quantitatively trace how opinions are transformed, filtered, or lost during summarization. They also develop Co-creation Provenance Lab, an interactive tool enabling human-AI collaborative oversight. Empirical evaluation on Canada’s national AI strategy consultation reveals that official summaries underperform a random baseline in coverage, with over 15% of participants effectively excluded and dissenting groups facing exclusion rates as high as 33%–88%, thereby demonstrating the framework’s effectiveness in enhancing the fidelity and fairness of policy summarization.

Technology Category

• Natural Language Processing: Summarization
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Humans and AI: Learning Human Values and Preferences

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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, causal inference and semantic analysis that tracks how individual public submissions are transformed, filtered or lost through AI-mediated summarization. Applied to Canada's 2025-2026 national AI Strategy consultation ($n = 5{,}253$ respondents across two independent policy topics), the framework reveals that both official government summaries underperform a random-participant baseline ($-9.1\%$ and $-8.0\%$ coverage degradation), with $16.9\%$ and $15.3\%$ of participants effectively excluded. Exclusion concentrates in clusters expressing dissent, scepticism and critique of AI ($33$-$88\%$ exclusion rates). Brevity, semantic isolation and rhetorical register independently predict representational outcome. An accompanying open-source interactive tool, the Co-creation Provenance Lab, enables policymakers to audit and iteratively improve summaries, establishing genuine human-in-the-loop oversight at scale.

Problem

• participatory provenance
• AI-mediated public consultation
• representational auditing
• input fidelity
• public input summarization

Innovation

• participatory provenance
• optimal transport
• causal inference
• semantic analysis
• representational auditing