Race, Gender, and Age Biases in Biomedical Masked Language Models (2023)

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.

## introduction social biases based on race, gender, and age cause healthcare disparities. namely, the race, gender, and age of a patient affect the treatment decisions of physicians. for instance, african american patients with coronary artery disease are less likely than white american patients to undergo cardiac catheterization, a life-saving procedure that corrects clogged arteries or irregular heartbeats @xcite . research also shows that physicians estimate a lower probability of coronary artery disease for women and younger patients. hence, african american women are less likely to be referred for cardiac catheterization than white american men @xcite . in an attempt to identify and eliminate healthcare disparities, implicit bias has been studied in-depth in real-world patient-provider interactions in both the emergency department @xcite and medical assessment of physicians on computersimulated patients @xcite . despite such efforts, these stereotypes continue to prevail and are unconsciously reflected in clinical notes and biomedical texts. following the recent releases and success of pretrained models in various domains, researchers introduced pre-trained models trained on large-scale biomedical corpora @xcite @xcite . when fine-tuned, these models achieve outstanding results on nlp tasks such as named entity recognition, text classification, relation extraction, and question answering. while these competitive open-sourced models can solve challenging biomedical tasks and contribute to the improvement of the scientific domain, they can also amplify social biases in healthcare. to identify such stereotypes, we examine social biases existing in the biomedical pre-trained models. we define bias as a tendency to associate a particular group with an illness in generated sentences and examine, given a bias, with which illness a model associates more. first, prompts are manually curated based on evidence-based practice. then, the models fill in the masked prompts. we observe the words pertinent to illness, such as "cancer" and "diabetes." lastly, a case study of the biases in coronary artery disease diagnoses and treatments is undertaken. in summary, our contributions are: (1) we in-vestigate biases in biomedical masked language models with manually curated prompts. the experimental results show that bert is less biased than the biomedical models in race and age and that each model associates distinct illnesses with a patient regardless of the bias. (2) we study whether the models associate a specific illness and a treatment with a particular bias. we use two bias metrics and demonstrate the challenges in measuring bias. ## method we investigate the influences of biases on the biomedical pre-trained language models by identifying associations between generated tokens and biased terms. first, we curate prompts grounded on evidence-based medicine. next, we compare the diagnosis predictions of a model based on race, gender, and age biases. ## experimental results we compare the prediction results among biomedical language models (lms) and analyze the association between illnesses and biases. as shown in to better quantify overlaps within biases, we measure the text overlap scores of each model, and the results are shown in the text overlap scores of all models in an interesting observation is that the three biomedical models, biobert, clninicalbert, and clinical longformer display the highest overlap scores in the gender bias and the lowest in the racial bias. on the contrary, the baseline bert exhibits an opposite result: the gender bias has the least overlapping tokens. we infer that biomedical models are less likely to predict different diagnoses based on gender than bert. finally, each model reveals a different tendency to predict an illness of a given patient. biobert predicts "malaria" with the highest scores across all biases except for the male bias. clinicalbert generates "pneumonia" most times except for asians. as for clinical longformer, the top 1 diagnosis is "cancer" for age and gender biases and "diabetes" for racial bias. this observation suggests that each model associates a specific illness to all patients irrespective of bias and that a model choice determines the prediction of diagnosis. ## conclusion we explore whether biases in clinical practice are reflected in pre-trained biomedical lms. the tendency in diagnosis predictions of the models is analyzed, and the overlaps in the predictions across biases are compared. as a case study, we measure bias in associating coronary artery disease with gender/age and cardiovascular procedures with gen