Marion Boulicault (University of Edinburgh): ‘Sex in the medical machine: How algorithms can entrench bioessentialism in precision medicine’

Abstract: Precision medicine advocates claim that machine learning will free us from a crude “one size fits all” approach: instead of basing decisions on comparisons to the “average patient,” precision medicine tools will offer customized predictions, diagnoses, and treatments based on an individual’s lifestyle, environment, and genetic make-up. Those developing such tools are, with increasing frequency, stratifying their models by sex or including sex as a predictor. The development and use of “pink” and “blue” algorithms is heralded as the “gateway” to a precision medicine future (Ferretti et al. 2018).

The embrace of sex stratification stands in contrast to discussions of race corrections in biomedical algorithms. Recent scholarship in philosophy of science and STS argues that race-based corrections reinforce stereotypes about biological differences between groups; systematically mischaracterize risk for non-white groups; and wrongly suggests that race, rather racism, is a cause of health disparities. In this talk, I argue that sex-stratification calls for similar scrutiny.

Using Alzheimer’s disease research as an example, I discuss how the move toward sex-specific “pink” and “blue” algorithms reinforces biological sex essentialist assumptions and their attendant harms. While one might assume that sex essentialism only enters into science when we have explicit theories about sex differences or make claims about the causal role of sex in producing an outcome, we show how essentialist biases can become entrenched and obfuscated when they’re incorporated in purportedly “theory-free”, “prediction only” algorithms. While the inclusion of sex in these algorithms is presented as necessary step towards a precision medicine future, we show that sex-specific algorithms can reproduce “cookie cutter” solutions and stereotypes, reanimate discredited sex difference theories, and trap us in a cycle of pink and blue predictive algorithms.

Citation: Ferretti, Maria, et al. 2018. “Sex Differences in Alzheimer Disease — the Gateway to Precision Medicine.” Nature Reviews Neurology: 457–69. https://doi.org/10.1038/s41582-018-0032-9.

Marion Boulicault is a Lecturer (Assistant Professor) at the University of Edinburgh, and the Director of Interdisciplinary Research and Community at the Harvard GenderSci Lab.

This event will take place in person on LSE’s campus. However, those unable to attend in person will have the option of taking part online.

To join online just follow these instructions:

• Download Zoom
• Register in advance for this meeting: https://lse.zoom.us/meeting/register/tZIvduisrTMsGt0r9-Ckdnf-gSVzbM2hnz4c

Please note that these events are routinely recorded, with the edited footage being made publicly available on our website and YouTube channel. We will only record the audio, the slides and the speaker and will not include the Q&A section. However, any question asked during the talk itself will feature in the final edit.