Sex in the medical machine: How algorithms can entrench bioessentialism in precision medicine

Machine learning offers new possibilities for developing more precise diagnostics and treatments, but the increasing use of sex stratification in precision medicine algorithms raises concerns. Using Alzheimer's disease (AD) research as an example in which machine learning approaches are applied to a heterogenous, socially patterned disease, this paper examines how the move toward sex-specific "pink" and "blue" algorithms reinforces biological sex essentialist assumptions and their attendant harms. We analyze three examples of sex-stratified algorithmic approaches in AD research, and identify three interacting processes—effacing contested knowledge, obscuring social factors, and ossifying binary sex categories—that can occur when binary sex variables are incorporated into predictive models. These case studies demonstrate that even in models intended to be causally agnostic, sex categories are likely to be interpreted as decontextualized, self-evident health determinants in a manner that can imply causality of biological sex. We call for establishing ethical norms and empirical standards for including gender/sex variables in precision medicine algorithms to avoid perpetuating crude ontologies of sex and gender that undermine both scientific validity and health justice.

Ichikawa and Boulicault are joint first authors of the paper; please cite as (Ichikawa and Boulicault et al. forthcoming); contact S. Richardson for advance manuscript.