Sex trouble: Sex/gender slippage, sex confusion, and sex obsession in machine learning using electronic health records.
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ABSTRACT:
SUBMITTER: Albert K
PROVIDER: S-EPMC9403398 | biostudies-literature | 2022 Aug
REPOSITORIES: biostudies-literature
Publications
Sex trouble: Sex/gender slippage, sex confusion, and sex obsession in machine learning using electronic health records.
Albert Kendra K Delano Maggie M
Patterns (New York, N.Y.) 20220812 8
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 perpetuating harm. In this perspective, we identify and discuss three factors to consider when working with sex/gender in research: "sex/gender slippage," the frequent substitution of sex and sex-related t ...[more]
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