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The rapid adoption of generative language models has brought aboutsubstantial advancements in digital communication, while simultaneously raisingconcerns regarding the potential misuse of AI-generated content. Althoughnumerous detection methods have been proposed to differentiate between AI andhuman-generated content, the fairness and robustness of these detectors remainunderexplored. In this study, we evaluate the performance of severalwidely-used GPT detectors using writing samples from native and non-nativeEnglish writers. Our findings reveal that these detectors consistentlymisclassify non-native English writing samples as AI-generated, whereas nativewriting samples are accurately identified. Furthermore, we demonstrate thatsimple prompting strategies can not only mitigate this bias but alsoeffectively bypass GPT detectors, suggesting that GPT detectors mayunintentionally penalize writers with constrained linguistic expressions. Ourresults call for a broader conversation about the ethical implications ofdeploying ChatGPT content detectors and caution against their use in evaluativeor educational settings, particularly when they may inadvertently penalize orexclude non-native English speakers from the global discourse. The publishedversion of this study can be accessed at:www.cell.com/patterns/fulltext/S2666-3899(23)00130-7

GPT detectors are biased against non-native English writers