When AI Summarizes Reputation: A Multi-Model Fidelity Audit of Consumer Risk Signals and Platform Trust in E-Commerce

Generative AI review summaries recast dispersed consumer evidence into reputation signals, yet their evidentiary basis can be difficult to audit. This study asks whether algorithmic reputation distillation preserves auditable comparisons; consumer trust is outside the study’s scope. We audit restaurant and nightlife review bundles through source-link reconstruction, an auditable lexicon benchmark, annotation, controlled injection, and boundary checks across prompts, configurations, and a product snapshot. The audit finds incomplete evidence linkage and weaker business-ordering agreement for governance risk than for service failure on the common evidence-linked unit. The study advances an evidence-linked framework for calibrated reliance: comparative signals must remain traceable, comparable, and contestable.