Claim 119 · research-integrity · connection

There is a fairness tension between detection and equity: Bietti & Bangerter 2026 note LLMs can support non-native English speakers by reducing linguistic barriers, yet the detection tools used to police LLM use are biased against non-native English writers (Liang et al., cited in She 2026).

Supported

E19 states that LLMs can support non-native English speakers by reducing linguistic barriers, and E21 shows She 2026 citing Liang et al. 2023, whose title states 'GPT detectors are biased against non-native English writers.'

Written by Kimi K3 via Ollama Cloud · checked by GLM-5.3 via Ollama Cloud · 1 Oct, 07:53

Source chain

A

Every quote below was checked, without a model, to appear verbatim in its source.

  1. 01

    “They can support non-native English speakers by reducing linguistic barriers”

    Full-text passage · no page number · evidence E19

    Passage read from www.ebi.ac.uk, which may be a preprint rather than the published version.

    Lucas Manuel Bietti 2026: Will the widespread use of large language models in scientific writing undermine scientists’ critical thinking?GreenDOI ↗
  2. 02

    “GPT detectors are biased against non-native English writers.”

    Full-text passage · p. 10 · evidence E21

    9. Naddaf, M. (2025). AI tool detects LLM-generated text in research papers and peer reviews. Nature. https://doi.org/10.1038/d41586-025-02936-6. 10. Liang, W., Yuksekgonul, M., Mao, Y., Wu, E., and Zou, J. (2023). GPT detectors are biased against non-native English writers. Patterns 4, 100779. https://doi.org/10.1016/j.patter.2023.100779. 11. The Gentle Singularity - Sam Altman https://blog.samaltman.com/the-gentle-singularity. 12. Using AI to accelerate scientific discovery | The Center for Brains, Minds & Machines https://cbmm.mit.edu/video/using-ai-accelerate-scientific-discovery. 13. Dario Amodei — Machines of Loving Grace https://www.darioamodei.com/essay/machines-of-loving-grace. 14. Shumailov, I., Shumaylov, Z., Zhao, Y., Papernot, N., Anderson, R., and Gal, Y. (2024). AI models collapse when trained on recursively generated data. Nature 631, 755–759. https://doi.org/10.1038/s41586-024-07566-y. 15. Guo, Y., Shang, G., Vazirgiannis, M., and Clavel, C. (2024). The Curious Decline of Linguistic Diversity: Training Language Models on Synthetic Text. In Findings of the Association for Computational Linguistics: NAACL 2024, K. Duh, H. Gomez, and S. Bethard, eds.

    Passage read from www.biorxiv.org, which may be a preprint rather than the published version.

    Richard She 2026: Fine-Grained Detection of AI-Generated Writing in the Biomedical LiteratureGreenDOI ↗