RESUMO
In their recent perspective published in Patterns, Maggie Delano and Kendra Albert highlight the limitations of sex and gender data classification in health systems and show how this contributes to the marginalization of trans and non-binary individuals. They provide recommendations to improve incorporating gender data into healthcare algorithms. Here they discuss their collaboration and how it enabled this cross-disciplinary research.
RESUMO
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 medical machine learning research using electronic health records, with a specific focus on HIV risk prediction. Finally, we offer recommendations about how machine learning researchers can engage more carefully with questions of sex/gender.
