Seeds of Stereotypes: A Large-Scale Textual Analysis of Race and Gender Associations with Diseases in Online Sources

arXiv:2405.05049v1 Announce Type: new
Abstract: Background Advancements in Large Language Models (LLMs) hold transformative potential in healthcare, however, recent work has raised concern about the tendency of these models to produce outputs that display racial or gender biases. Although training data is a likely source of such biases, exploration of disease and demographic associations in text data at scale has been limited.
Methods We conducted a large-scale textual analysis using a dataset comprising diverse web sources, including Arxiv, Wikipedia, and …

abstract analysis arxiv biases cs.cl data diseases gender healthcare however language language models large language large language models llms race race and gender racial scale stereotypes textual training training data type work