In a peer-reviewed paper recently published in Big Data and Society and led by Kelsey Ichikawa and Marion Boulicault, GenderSci Lab members explore the use of sex stratification in applications of machine learning in medical research and argue 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. 

As governments and private companies take up the charge of deep space travel and occupation, a question arises. If humans are to live away from Earth, how will they reproduce themselves? Here, the GenderSci Lab’s Jonathan Galka examines the evidence for human reproduction in Space, and what the state of the data tells us about who, according to governments and research establishments, gets imagined as a rightful future inhabitant of Space.