Confounding factors need to be accounted for in assessing bias by machine learning algorithms
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Biochemistry, Genetics and Molecular Biology,General Medicine
Link
https://www.nature.com/articles/s41591-022-01847-7.pdf
Reference2 articles.
1. Seyyed-Kalantari, L. et al. Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nat. Med. 27, 2176–2182 (2021).
2. Seyyed-Kalantari, L., Liu, G., McDermott, M., Chen, I. Y. & Ghassemi, M. CheXclusion: fairness gaps in deep chest X-ray classifiers. In Pacific Symposium on Biocomputing 2021 (eds Altman, R. B. et al.) 232–243 (World Scientific Publishing, 2021).
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