Assessing Racial and Ethnic Bias in Text Generation for Healthcare-Related Tasks by ChatGPT ¹

Abstract

Large Language Models (LLM) are AI tools that can respond human-like to voice or free-text commands without training on specific tasks. However, concerns have been raised about their potential racial bias in healthcare tasks. In this study, ChatGPT was used to generate healthcare-related text for patients with HIV, analyzing data from 100 deidentified electronic health record encounters. Each patient’s data were fed four times with all information remaining the same except for race/ethnicity (African American, Asian, Hispanic White, Non-Hispanic White). The text output was analyzed for sentiment, subjectivity, reading ease, and most used words by race/ethnicity and insurance type. Results showed that instructions for African American, Asian, Hispanic White, and Non-Hispanic White patients had an average polarity of 0.14, 0.14, 0.15, and 0.14, respectively, with an average subjectivity of 0.46 for all races/ethnicities. The differences in polarity and subjectivity across races/ethnicities were not statistically significant. However, there was a statistically significant difference in word frequency across races/ethnicities and a statistically significant difference in subjectivity across insurance types with commercial insurance eliciting the most subjective responses and Medicare and other payer types the lowest. The study suggests that ChatGPT is relatively invariant to race/ethnicity and insurance type in terms of linguistic and readability measures. Further studies are needed to validate these results and assess their implications.

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