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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 \u201cpink\u201d and \u201cblue\u201d 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. 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