J. Minot, N. Cheney, Marc E. Maier, Danne C. Elbers, C. Danforth, P. Dodds
{"title":"文本数据的可解释偏见缓解:在保持分类性能的同时减少患者笔记中的性别化","authors":"J. Minot, N. Cheney, Marc E. Maier, Danne C. Elbers, C. Danforth, P. Dodds","doi":"10.1145/3524887","DOIUrl":null,"url":null,"abstract":"Medical systems in general, and patient treatment decisions and outcomes in particular, can be affected by bias based on gender and other demographic elements. As language models are increasingly applied to medicine, there is a growing interest in building algorithmic fairness into processes impacting patient care. Much of the work addressing this question has focused on biases encoded in language models—statistical estimates of the relationships between concepts derived from distant reading of corpora. Building on this work, we investigate how differences in gender-specific word frequency distributions and language models interact with regards to bias. We identify and remove gendered language from two clinical-note datasets and describe a new debiasing procedure using BERT-based gender classifiers. We show minimal degradation in health condition classification tasks for low- to medium-levels of dataset bias removal via data augmentation. Finally, we compare the bias semantically encoded in the language models with the bias empirically observed in health records. This work outlines an interpretable approach for using data augmentation to identify and reduce biases in natural language processing pipelines.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"240 1","pages":"1 - 41"},"PeriodicalIF":0.0000,"publicationDate":"2021-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":"{\"title\":\"Interpretable Bias Mitigation for Textual Data: Reducing Genderization in Patient Notes While Maintaining Classification Performance\",\"authors\":\"J. Minot, N. Cheney, Marc E. Maier, Danne C. Elbers, C. Danforth, P. Dodds\",\"doi\":\"10.1145/3524887\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Medical systems in general, and patient treatment decisions and outcomes in particular, can be affected by bias based on gender and other demographic elements. As language models are increasingly applied to medicine, there is a growing interest in building algorithmic fairness into processes impacting patient care. Much of the work addressing this question has focused on biases encoded in language models—statistical estimates of the relationships between concepts derived from distant reading of corpora. Building on this work, we investigate how differences in gender-specific word frequency distributions and language models interact with regards to bias. We identify and remove gendered language from two clinical-note datasets and describe a new debiasing procedure using BERT-based gender classifiers. We show minimal degradation in health condition classification tasks for low- to medium-levels of dataset bias removal via data augmentation. Finally, we compare the bias semantically encoded in the language models with the bias empirically observed in health records. This work outlines an interpretable approach for using data augmentation to identify and reduce biases in natural language processing pipelines.\",\"PeriodicalId\":72043,\"journal\":{\"name\":\"ACM transactions on computing for healthcare\",\"volume\":\"240 1\",\"pages\":\"1 - 41\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-03-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"21\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM transactions on computing for healthcare\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3524887\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM transactions on computing for healthcare","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3524887","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Interpretable Bias Mitigation for Textual Data: Reducing Genderization in Patient Notes While Maintaining Classification Performance
Medical systems in general, and patient treatment decisions and outcomes in particular, can be affected by bias based on gender and other demographic elements. As language models are increasingly applied to medicine, there is a growing interest in building algorithmic fairness into processes impacting patient care. Much of the work addressing this question has focused on biases encoded in language models—statistical estimates of the relationships between concepts derived from distant reading of corpora. Building on this work, we investigate how differences in gender-specific word frequency distributions and language models interact with regards to bias. We identify and remove gendered language from two clinical-note datasets and describe a new debiasing procedure using BERT-based gender classifiers. We show minimal degradation in health condition classification tasks for low- to medium-levels of dataset bias removal via data augmentation. Finally, we compare the bias semantically encoded in the language models with the bias empirically observed in health records. This work outlines an interpretable approach for using data augmentation to identify and reduce biases in natural language processing pipelines.