专业润色让真人写作被误判为AI生成，暴露检测工具的严重偏见。
Style as a Confound: False Positives in AI Detection of Non-Native Academic Writing
- 用13.5万对润色前后稿件对比，控制作者和内容变量
- 13种检测器假阳性率从0%到100%，编辑越多得分越升或越降
- 润色风格是关键干扰项，影响学术公平性与检测可靠性
AI文本检测工具在学术领域应用日益广泛，但其结果是否反映真实AI生成，还是受精致英语表达等语言特征影响尚不明确。以往研究指出非母语写作存在高假阳性率，但因主题、领域和写作风格差异混杂而难以界定。本研究利用2018-2025年某专业英文润色服务提供的135,389对文档（非母语原稿及其润色版），在保持作者和内容不变的前提下，评估润色对检测结果的影响。结果显示，13种检测器对人工写作的假阳性率差异极大，范围为0.0%至100.0%；同一润色操作在不同检测器中导致分数上升或下降。尤其值得注意的是，分数变化与润色程度显著相关。研究揭示专业润色风格是检测结果的关键混淆变量，而非完全分离文本来源与语言风格，引发学术评价中公平性与可靠性的担忧。
原文摘要 · Abstract (English)
AI text detectors are increasingly employed in academic settings, but it remains unclear whether their outputs reflect AI authorship itself or broader linguistic features associated with polished academic English. Previous studies have reported high false-positive rates (FPRs) for non-native English writing, but population-level comparisons confound authorship with differences in topic, domain, and writing style. Professional editing provides a useful setting for examining this issue because it changes the linguistic form of manuscripts while preserving authorship and content. We examined 135,389 document pairs from a professional English editing service (2018-2025), comprising non-native manuscripts and their native-edited versions, to assess how editing affects detector responses controlling for content and authorship. For the 13 AI text detectors, FPRs for human-written texts varied widely, from 0.0% to 100.0%. Responses varied across detectors: the same edits increased AI scores in some detectors but decreased them in others. Notably, score changes correlated with the extent of editing. The findings identify professional editing style as a key confounding variable in AI detector outputs, rather than establishing a full separation of text origin from linguistic style, raising concerns about fairness and reliability in academic settings.
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