Sex trouble: Sex/gender slippage, sex confusion, and sex obsession in machine learning using electronic health records.

ABSTRACT: 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 terms for gender and vice versa; "sex confusion," the fact that any given sex variable holds many different potential meanings; and "sex obsession," the idea that the relevant variable for most inquiries related to sex/gender is sex assigned at birth. We then explore how these phenomena show up in me

SUBMITTER: Albert K 

PROVIDER: S-EPMC9403398 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature