Assessing Racial and Ethnic Bias in Text Generation by Large Language Models for Health Care–Related Tasks: Cross-Sectional Study

A total of 4 LLMs were relatively invariant to race/ethnicity in terms of linguistic and readability measures. While our study used proxy linguistic and readability measures to investigate racial and ethnic bias among 4 LLM responses in a health care-related task, there is an urgent need to establish universally accepted standards for measuring bias in LLM-generated responses. Further studies are needed to validate these results and assess their implications.