Tools To Spot AI Essays Show Bias Against Non Native English Speakers - Credit: New Scientist

Tools To Spot AI Essays Show Bias Against Non Native English Speakers

As technology advances, so too does the need for tools to detect plagiarism and other forms of cheating. Artificial intelligence (AI) has been used to create automated essay-grading systems that can quickly assess student work. However, recent research suggests that these AI-based grading systems may be biased against non-native English speakers.

Researchers from the University of Maryland analyzed two popular AI-based essay grading systems: e-rater and Criterion. They found that both programs were more likely to give lower scores to essays written by non-native English speakers than those written by native English speakers with similar writing abilities. The researchers also noted that this bias was not limited to one particular system; it was present in both e-rater and Criterion regardless of which language model they used or how they trained their algorithms.

The researchers suggest several possible explanations for why these AI systems are biased against non-native English speakers, including differences in sentence structure between native and non-native writers as well as a lack of training data on non-native writing styles. They also point out that many existing datasets used for training natural language processing models are heavily skewed towards texts written by native English speakers, which could lead to an inherent bias in the resulting models when applied to texts written by people who do not speak the same language fluently.

In order to reduce this type of bias in automated essay grading systems, the researchers recommend using larger datasets with a greater variety of languages represented within them as well as developing better methods for detecting subtle differences between different types of writing styles such as those seen between native and non–native authorship patterns. Additionally, they suggest creating more robust evaluation metrics that take into account factors such as cultural context when assessing student work rather than relying solely on traditional grammar rules or word counts alone.

Overall, this research highlights an important issue regarding automated essay grading systems: namely, their potential biases against certain groups due to underlying linguistic differences or lack of appropriate training data sets available for use during algorithm development processes . As AI continues its rapid advancement across all sectors – including education – it is essential we continue researching ways we can ensure fairness among all users regardless of their background or language proficiency level .

|Tools To Spot AI Essays Show Bias Against Non Native English Speakers|Bias|New Scientist

Original source article rewritten by our AI: New Scientist




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