Sex in the Medical Machine

How "pink and blue" algorithms in Alzheimer's research risk hard-wiring outdated ideas about sex into precision medicine — based on a 2025 study in Big Data & Society.

Key terms

Access the Youtube video here.

  • The assumption that observed differences between males and females are rooted in fixed, unchanging biology, rather than in social, environmental, or historical context.

  • A machine learning model trained or run separately for male and female patients, on the premise that sex differences are large and consistent enough to warrant entirely distinct diagnostic pathways.

  • A term the GenderSci Lab uses to signal that gender and sex are deeply entangled and often cannot be cleanly separated in observed health data.

Discussion Questions

  1. The video introduces "pink and blue algorithms." In your own words, what problem does this term point to?

  2. The paper argues that treating sex as a machine learning input can "efface contested knowledge." Can you think of an example — medical or otherwise — where a debated claim gets treated as settled just because it's built into a technical system?

  3. How does the video suggest social factors might be mistaken for biological sex differences in Alzheimer's data?

  4. What might a "sex-contextualist" alternative to pink/blue algorithms look like in practice?

  5. Whose expertise might be needed to design a precision medicine algorithm that avoids the pitfalls described here?

Suggested Readings

  1. Ichikawa, Kelsey, Marion Boulicault, Alex Thinius, Marina DiMarco, Audrey R. Murchland, Ben Maldonado, Abigail S. Higgins, and Sarah S. Richardson. “Sex in the Medical Machine: How Algorithms Can Entrench Bioessentialism in Precision Medicine”. Big Data and Society 12, no. 4 (2025).

  2. Ichikawa, K. and Richardson, S. “Sex in the Medical Machine: The GenderSci Lab Analyzes the Algorithmic Future of Sex-Based Medicine.” GenderSci Lab Blog. 27 January 2026. https://www.genderscilab.org/blog/sex-in-the-medical-machine

  3. Pape, M., Miyagi, M., Ritz, S. A., Boulicault, M., Richardson, S. S., & Maney, D. L. (2024). Sex contextualism in laboratory research. Cell, 187(6), 1316–1326.

Frequently Asked Questions

  • Yes — no prior background in machine learning is required. It works well as a stand-alone assignment in intro STS, gender studies, health policy, or "AI and society" courses.

  • History and philosophy of science, science & technology studies (STS), bioethics, computer science and machine learning, biomedical informatics, gender studies, and public health.

  • Alzheimer's disease, chosen because it shows pronounced gender/sex differences in prevalence and because several current research programs are already building sex-stratified diagnostic algorithms for it.

  • No. The paper argues for "sex contextualism" — taking sex seriously as one factor among many, understood in social and biological context — rather than treating it as a stand-alone, deterministic category.

Suggested Citations

SUGGESTED VIDEO CITATION

GenderSci Lab. (2026). Algorithmic futures for women's health? [Video]. YouTube. https://www.youtube.com/watch?v=9TqL_cX1OdU.

SUGGESTED WEBPAGE CITATION

GenderSci Lab. (2026). Sex in the medical machine. https://www.genderscilab.org/sex-in-the-medical-machine.