In this video, the GenderSci Lab explores the use of sex stratification in applications of machine learning in medical research, arguing that current practices risk embedding biological sex essentialist assumptions into medical science. These practices include the creation of distinct algorithms for males and females (what we call “pink and blue algorithms”), the use of machine learning to identify distinct male and female patterns in disease, and the incorporation of gender/sex variables as predictors in algorithms for disease risk and detection. 

This video was created by the GenderSci Lab, with the support of the Disruption Society, as part of a Robert Wood Johnson Foundation-funded grant.