This Whole Thing Smacks of Gender

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

“…We introduced the term sex/gender slippage in an earlier work to reflect the way some researchers switched back and forth between the terms sex and gender within their research articles and equations. 59 However, as we will explore in this perspective, the presumed concordance of sex and gender as reflected by sex/gender slippage is embedded in the medical system and society at large, even if not explicitly discussed in these terms. One parallel in feminist writing is Judith Butler’s idea of a “stable gender” in the “heterosexual matrix.” 63 , 64 To some extent, the modern fields of gender and transgender studies exist to unpack, critique, or complicate the easy switch between sex and gender, in addition to trying to figure out what sex and gender refer to.…”

“…Additionally, this study incorporates data from an external anesthesia-monitoring system and other hardware that could have their own embedded sex/gender-related biases. 59 , 90 Sex/gender may also be relevant if related laboratory testing or vital signs use sex/gender-based reference intervals or sex/gender offsets. 47 , 72 If the model is built such that the EHR sex field is the relevant one for determining such thresholds/offsets, it could be wrong for whole subsections of the population without any clinician being the wiser.…”

“…However, it is only the interpretability of their algorithm that allows us to analyze it. To the extent that ML systems may at some point be used to produce so called “dimensionless parameters,” 59 , 91 the failure to specify how questions of sex/gender might affect these numbers could have deleterious effects on transgender patients. The closer that ML based on EHR data gets to prescribing clinical care without allowing for a care team to determine whether the assumptions made as part of algorithmic development are correct for the patient, the more significant the risks are to individual patients who may “deviate” from the expectations of the people who built the systems.…”

“…22 , 77 , 107 (6) Consider bias from sources other than sex and gender-identity fields. 46 , 59 , 85 , 100 , 101 (7) Audit model performance for subgroups without presuming or essentializing differences. 46 , 57 , 108 , 109 , 110 , 111 …”

Two researchers share how their cross disciplinary collaboration enables work to guide the future of data science

“…We were approached by an editor at Patterns after we wrote an article about sex/gender and weight scales for the Fairness, Accountability and Transparency Conference in 2021. 2 The editor invited us to expand upon that paper, and we decided we wanted to spend time focusing on how sex/gender are used in machine learning models broadly.…”