Quantifying 60 Years of Gender Bias in Biomedical Research with Word Embeddings (2020)

Abstract: Gender bias in biomedical research can have an adverse impact on the health of real people. For example, there is evidence that heart disease-related funded research generally focuses on men. Health disparities can form between men and at-risk groups of women (i.e., elderly and low-income) if there is not an equal number of heart disease-related studies for both genders. In this paper, we study temporal bias in biomedical research articles by measuring gender differences in word embeddings. Specifically, we address multiple questions, including, How has gender bias changed over time in biomedical research, and what health-related concepts are the most biased? Overall, we find that traditional gender stereotypes have reduced over time. However, we also find that the embeddings of many medical conditions are as biased today as they were 60 years ago (e.g., concepts related to drug addiction and body dysmorphia).

## introduction it is important to develop gender-specific bestpractice guidelines for biomedical research @xcite . if research is heavily biased towards one gender, then the biased guidance may contribute towards health disparities because the evidence drawn-on may be questionable (i.e., not well studied). for example, there is more research funding for the study of heart disease in men @xcite . therefore, the at-risk populations of older women in low economic classes are not as well-investigated. therefore, this opens up the possibility for an increase in the health disparities between genders. among informatics researchers, there has been increased interest in understanding, measuring, and overcoming bias associated with machine learning methods. researchers have studied many applica-tion areas to understand the effect of bias. for example, @xcite found that the google image search application is biased @xcite . specifically, they found an unequal representation of gender stereotypes in image search results for different occupations (e.g., all police images are of men). likewise, ad-targeting algorithms may include characteristics of sexism and racism @xcite . @xcite found that the names of black men and women are likely to generate ads related to arrest records. in healthcare, much of the prior work has studied the bias in the diagnosis process made by doctors @xcite . there have also been studies about ethical considerations about the use of machine learning in healthcare @xcite . it is possible to analyze and measure the presence of gender bias in text. @xcite analyzed the presence of well-known gender stereotypes over the last 100 years. @xcite shown that gender blindness and stereotyped preconceptions are the key cause for gender bias in medicine. @xcite studied the genderbased linguistic differences in physician trainee evaluations of medical faculty. @xcite measured the implicit and explicit gender bias among health care professionals and surgeons. @xcite quantified the exclusion of females in clinical studies at scale with automated data extraction. recently, researchers have studied methods to quantify gender bias using word embeddings trained on biomedical research articles @xcite . @xcite shown that the resulting embeddings capture some well-known gender stereotypes. moreover, the embeddings exhibit the stereotypes at a lower rate than embeddings trained on other corpora (e.g., wikipedia). however, to the best of our knowledge, there has not been an automated temporal study in the change of gender bias. in this paper, we look at the temporal change of gender bias in biomedical research. to study social biases, we make use of word embeddings trained on different decades of biomedical research articles. the two main question driving this work are, in what ways has bias changed over time, and are there certain illnesses associated with a specific gender? we leverage three computational techniques to answer these questions, the word embedding association test (weat) @xcite , the embedding coherence test (ect) @xcite , and relational inner product association (ripa) @xcite . to the best of our knowledge, this will be the first temporal analysis of bias of word embeddings trained on biomedical research articles. moreover, to the best of our knowledge, this is the first analysis that measures the gender bias associated with individual biomedical words. our work is most similar to @xcite . @xcite study the temporal change of both gender and racial biases using word embeddings. our work substantially differs in three ways. first, this paper is focused on biomedical literature, not general text corpora. second, we analyze gender stereotypes using three distinct methods to see if the bias is robust to various measurement techniques. third, we extend the study beyond gender stereotypes. specifically, we look at bias in sets of occupation words, as well as bias in mental health-related word sets. moreover, we quantify the bias of individual occupational and mental health-related words. in summary, the paper makes the following contributions: ## related work in this section, we discuss research related to the three major themes of this paper: gender disparities in healthcare, biomedical word embeddings, and bias in natural language processing (nlp). ## dataset we analyze pubmed-indexed titles and abstracts published anytime between 1960 and 2020. the total number of articles per decade are shown in ## method we train the skip-gram model on pubmed-indexed titles and abstracts from 1960 to 2020. the hyperparameters of the skip-gram model are optimized independently for each decade. next, given the best set of embeddings for each decade, we explore three different techniques to measure bias: the word embedding association test (weat), the embedding coherence test (ect), and the relational inner product association (ripa). each method allows us to quantify bias in different ways, such as comparing multiple sets of words (e.g., comparing the bias with respect to career vs family), comparing a single set of words (e.g., occupations), and measuring the bias of individual words (e.g., nurse). in this section, we briefly discuss the procedure we used to train the word embeddings, ## results in this section, we present the results of our study in four parts. first, we report the embedding quality using umls-sim. second, we study the temporal bias of traditional gender stereotypes, such as career vs family and strong vs weak. ideally, we want to understand how, and which, stereotypes have changed over time. to understand the biased stereotypes, we make use of the weat method. third, we look at whether occupational and mental health-related words are biased, and how the bias has changed over time. for this result, we only use a single set of target words. thus, we make use of ect. fourth, we use ripa to find the most biased words for each gender in each decade. ## discussion in this section, we discuss the impact of the results on two stakeholders of this research: bionlp researchers and general biomedical researchers. furthermore, we discuss the limitations of focusing on binary gender (male vs female). ## conclusion in this paper, we studied the historical bias present in word embeddings from 1960 to 2020. in summary, we found that while some biases have shown a consistently decrease over time (e.g., strong vs weak), others have stayed relatively static, or worse, increased (e.g., intelligence vs appearance). moreover, we found that the gender bias towards occupations has substantially changed over time, showing that in the past, there was more gender bias associated with certain jobs. there are two major avenues for future work. first, this work quantified various aspects of gender bias over time. however, we do not know why the bias is present in the word embeddings. for example, is the word "hysteria" biased in 2010 because researchers are associating it with women implicitly, or is it that researchers are studying the historical usage of the diagnosis to ensure the diagnosis is not made because of implicit bias in the future? thus, our future work will focus on causal studies of bias in biomedical literature. second, we simply independently trained skip-gram word embeddings for each decade. however, recent work has shown that dynamic embeddings, rather than static (decade-specific), perform better with regard to analyzing public perception over time @xcite . future work will focus on developing new techniques to study bias temporally. moreover, many techniques may depend on the magnitude of the bias, therefore, we plan to analyze the circumstances in which one embedding approach may measure bias (e.g., skip-gram) better than another (e.g., dynamic embeddings). @xcite