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Sex trouble: Sex/gender slippage, sex confusion, and sex obsession in machine learning using electronic health records
False assumptions that sex and gender are binary, static, and concordant are deeply embedded in the medical system. As machine learning researchers use medical data to build tools to solve novel problems, understanding how existing systems represent sex/gender incorrectly is necessary to avoid perpe...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Elsevier
2022
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9403398/ https://www.ncbi.nlm.nih.gov/pubmed/36033589 http://dx.doi.org/10.1016/j.patter.2022.100534 |