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.

翻译：人工智能正被越来越多地部署于政策咨询和参与式流程中，以综合大规模公众意见。然而，目前缺乏正式框架来审计这些摘要是否忠实代表源人群，这一问责缺口是现有AI可解释性、基础性及幻觉检测方法无法解决的，因为它们关注的是输出质量而非输入忠实度。本文提出"参与式溯源"：一个基于最优传输理论、因果推断和语义分析的测量框架，用于追踪个体公众提交内容如何通过AI中介摘要而被转换、过滤或丢失。将该框架应用于加拿大2025-2026年国家AI战略咨询（两个独立政策主题，共$n=5{,}253$名受访者），结果显示：官方政府摘要的表现均低于随机参与者基线（覆盖率分别下降$-9.1\%$和$-8.0\%$），有$16.9\%$和$15.3\%$的参与者被有效排除。排除现象集中在表达异议、怀疑和批评AI的群体中（排除率为$33$-$88\%$）。简洁性、语义孤立性和修辞语域独立预测代表性结果。配套的开源交互工具"共创溯源实验室"使政策制定者能够审计并迭代改进摘要，从而在大规模尺度上建立真正的人类在环监督机制。