How Language Bias Persists in Scientific Publishing Despite AI Tools
Date: June 16, 2025
Topics: Ethics, Equity, Inclusion, Generative AI
English continues to dominate scientific publishing and international conferences, creating a significant barrier for non-native English-speaking researchers. Despite advances in AI, particularly large language models (LLMs) like ChatGPT, language bias in peer review remains a persistent challenge.
Researchers at Stanford Graduate School of Education investigated this issue and found that AI tools alone don't eliminate bias against authors from non-English-speaking countries. Peer reviewers often infer an author's country of origin based on writing style or the use of AI-generated phrases, which can influence their evaluation of the scientific work.
The study analyzed nearly 80,000 peer reviews from a major computer science conference. Findings showed that while grammatical errors decreased after the introduction of ChatGPT, subtle biases stayed largely unchanged. Reviewers began to associate certain phrases common in AI-assisted writing — like “delve” — with non-native speakers, reinforcing stereotypes about research quality linked to language proficiency.
This research was supported by a seed grant from the Stanford Institute for Human-Centered AI and will be presented at the Association for Computing Machinery conference on Fairness, Accountability, and Transparency. The full paper, “‘You Cannot Sound Like GPT’: Signs of language discrimination and resistance in computer science publishing”, is available on arXiv.
Why focus on LLMs and language bias in scientific publishing?
The researchers noted that conversations around LLMs often place the responsibility on non-native English-speaking authors to "fix" their language, rather than addressing the biases of the readers and reviewers. In education research, it's well documented that linguistic bias often stems from the listener's or reader's attitudes, not just the speaker's or writer's language use.
This study applies that perspective to international science publishing, showing that even when authors use AI to improve language, reviewers' subconscious biases about language and origin influence their judgment.
Why hasn’t ChatGPT eliminated language bias?
Bias did not vanish because reviewers associate language quality with scientific quality, using language as a proxy for trustworthiness or expertise. After AI tools reduced obvious grammatical issues, reviewers began to recognize patterns or phrases typical of AI writing and inferred the author's background, reinforcing stereotypes.
These assumptions often reflected preconceived notions about researchers from certain countries, indicating that bias is less about language errors and more about underlying social perceptions.
Will AI be a democratizing force or deepen inequality?
Access to AI tools alone doesn’t guarantee fairness. The idea that technology inherently promotes equity overlooks the persistence of social hierarchies. Biases can reemerge in new forms, even when marginalized groups use the same tools as dominant groups.
This means that simply providing AI tools to non-native speakers won't fix deeper structural issues in scientific evaluation and publishing.
Key takeaways
- Language in scientific publishing is often read as a signal of race, class, and trustworthiness, not just content.
- English-only publishing has historical ties to colonialism and exclusion, which AI tools alone cannot undo.
- Biases serve as shortcuts for reviewers pressed for time, leading to judgments based on writing style or perceived author background rather than research merit.
- Addressing language bias requires changes in reviewer awareness and publishing practices, not just better language tools for authors.
The study encourages the scientific community to critically examine how language is used as a gatekeeping tool and to develop more equitable peer review systems. For those interested in the intersection of AI, language, and equity, this research offers important insights into the limits of technology as a fix for deep-rooted biases.
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