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        <title>Race, Gender, and Age Biases in Biomedical Masked Language Models</title>
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        <namePart type="given">Michelle</namePart>
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    <abstract>Biases cause discrepancies in healthcare services. Race, gender, and age of a patient affect interactions with physicians and the medical treatments one receives. These biases in clinical practices can be amplified following the release of pre-trained language models trained on biomedical corpora. To bring awareness to such repercussions, we examine social biases present in the biomedical masked language models. We curate prompts based on evidence-based practice and compare generated diagnoses based on biases. For a case study, we measure bias in diagnosing coronary artery disease and using cardiovascular procedures based on bias. Our study demonstrates that biomedical models are less biased than BERT in gender, while the opposite is true for race and age.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-acl.749</identifier>
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%0 Conference Proceedings
%T Race, Gender, and Age Biases in Biomedical Masked Language Models
%A Kim, Michelle
%A Kim, Junghwan
%A Johnson, Kristen
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F kim-etal-2023-race
%X Biases cause discrepancies in healthcare services. Race, gender, and age of a patient affect interactions with physicians and the medical treatments one receives. These biases in clinical practices can be amplified following the release of pre-trained language models trained on biomedical corpora. To bring awareness to such repercussions, we examine social biases present in the biomedical masked language models. We curate prompts based on evidence-based practice and compare generated diagnoses based on biases. For a case study, we measure bias in diagnosing coronary artery disease and using cardiovascular procedures based on bias. Our study demonstrates that biomedical models are less biased than BERT in gender, while the opposite is true for race and age.
%R 10.18653/v1/2023.findings-acl.749
%U https://aclanthology.org/2023.findings-acl.749
%U https://doi.org/10.18653/v1/2023.findings-acl.749
%P 11806-11815

Markdown (Informal)

[Race, Gender, and Age Biases in Biomedical Masked Language Models](https://aclanthology.org/2023.findings-acl.749) (Kim et al., Findings 2023)

• Race, Gender, and Age Biases in Biomedical Masked Language Models (Kim et al., Findings 2023)

ACL

• Michelle Kim, Junghwan Kim, and Kristen Johnson. 2023. Race, Gender, and Age Biases in Biomedical Masked Language Models. In Findings of the Association for Computational Linguistics: ACL 2023, pages 11806–11815, Toronto, Canada. Association for Computational Linguistics.