Side-by-side Comparison Amplifies Dialect Bias in Language Models

Language models (LMs) can exhibit biases based on variations in their dialects, even in the absence of a dialect label, a behavior known as covert dialect bias. In this work, we quantify covert dialect bias in online discourse by evaluating how LMs associate stereotypical traits (derived from social psychology research on racial bias) with intent-equivalent tweets in Standard American English (SAE) and African-American Vernacular English (AAVE). While prior work shows that LMs associate more neg

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ethics.ai (23 May 2026), “Side-by-side Comparison Amplifies Dialect Bias in Language Models,” evidence record 3832, https://ethics.ai/record/3832 (originally published by arXiv).

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