GPT detectors are biased against non-native English writers
2023/04/06 by Weixin Liang, Liang, Weixin, Mert Yüksekgönül +9 · 13 voices · 26 citations
Medicine · Computer Science · Social Sciences · #Artificial Intelligence in Healthcare and Education #Topic Modeling #Ethics and Social Impacts of AI
paper · pdf · doi:10.48550/arxiv.2304.02819
Abstract
The rapid adoption of generative language models has brought about substantial advancements in digital communication, while simultaneously raising concerns regarding the potential misuse of AI-generated content. Although numerous detection methods have been proposed to differentiate between AI and human-generated content, the fairness and robustness of these detectors remain underexplored. In this study, we evaluate the performance of several widely-used GPT detectors using writing samples from native and non-native English writers. Our findings reveal that these detectors consistently misclassify non-native English writing samples as AI-generated, whereas native writing samples are accurately identified. Furthermore, we demonstrate that simple prompting strategies can not only mitigate this bias but also effectively bypass GPT detectors, suggesting that GPT detectors may unintentionally penalize writers with constrained linguistic expressions. Our results call for a broader conversation about the ethical implications of deploying ChatGPT content detectors and caution against their use in evaluative or educational settings, particularly when they may inadvertently penalize or exclude non-native English speakers from the global discourse. The published version of this study can be accessed at: www.cell.com/patterns/fulltext/S2666-3899(23)00130-7
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Discussions
- GPT detectors are biased against non-native English writers [hn, 338 points, 274 comments]
- Many (all?) current ChatGPT detectors have not been adequately assessed for issues of algorithmic bias and therefore should not be used to accuse students of misconduct in their written work. This af [bsky, 64 points, 3 comments]
- arxiv.org/abs/2304.028... [bsky, 8 points, 1 comments]
- My understanding is that the detectors are not very good at this point (they're unreliable), and they're also biased against non-native speakers. So...use caution if you're going to use them. Paper o [bsky, 6 points, 1 comments]
- GPT detectors are biased against non-native English writers (2023) [hn, 2 points, 0 comments]
- In our Writing + AI workshops, I've mentioned that some research (arxiv.org/abs/2304.02819) has shown that AI detectors are biased against non-native English speakers. Reading more research today that [bsky, 1 points, 0 comments]
- Worse still, another study from Stanford showed that ZeroGPT and similar tools often flagged non-native English writing as AI simply because it lacked variation or complexity. arxiv.org/abs/2304.028 [bsky, 1 points, 0 comments]
- Hi Rachel, arxiv.org/abs/2304.02819 I found this after looking at a paper on the effectiveness of AI detectors, it gave glowing reports of Turnitin and Copyleaks (there were caveats, need to really re [bsky, 1 points, 1 comments]
- Aqui o estudo original GPT detectors are biased against non-native English writers Autors: Weixin Liang, Mert Yuksekgonul, Yining Mao, Eric Wu, James Zou arxiv.org/abs/2304.02819 [bsky, 0 points, 0 comments]
- GPT detectors are biased against non-native English writers arxiv.org/abs/2304.02819 [bsky, 0 points, 0 comments]
- 🚨 Studie warnt! KI-Erkennungssysteme stufen Texte von Menschen, deren Muttersprache nicht Englisch ist, fälschlicherweise als von einer KI geschrieben ein. KI-Texte können nicht zuverlässig erkannt [bsky, 0 points, 0 comments]
- 🤔 GPT detectors are biased against non-native English writers https://arxiv.org/abs/2304.02819 [bsky, 0 points, 0 comments]
- arxiv.org/abs/2304.02819 www.researchgate.net/publication/... teaching.unl.edu/ai-exchange/... [bsky, 0 points, 0 comments]
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