New research suggests that artificial intelligence (AI) language models used to evaluate essays may be biased against non-native English speakers. The study examined four commonly used AI writing assessment tools and found that they consistently scored essays written by non-native English speakers lower than those written by native English speakers, even when the quality of the essays was comparable.
The researchers believe that this bias stems from the fact that AI language models are trained on large datasets of English text written by native speakers, which can result in a narrow understanding of what constitutes “good” writing. As a result, non-native English speakers who use different grammar structures and vocabulary may be penalized by the AI models.
The findings have implications for educational institutions that use AI writing assessment tools to evaluate students’ work, as biased assessments could lead to unfair grading and negatively impact non-native English speakers’ academic progress. The researchers suggest that AI language models should be trained on more diverse datasets that include text written by non-native English speakers to reduce bias in writing assessments.
Overall, the study highlights the need for greater attention to bias in AI language models and the importance of ensuring that these models are developed and trained with diverse perspectives in mind.
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