Ward, Zina B. (2026) Natural Kinds and Machine Learning: The Case of Male and Female Brains. [Preprint]
![[img]](https://philsci-archive.pitt.edu/style/images/fileicons/text.png) |
Text
Ward BJPS ML_Sex_Brains.pdf Download (491kB) |
Abstract
Neuroscientists are currently at loggerheads about whether to draw a distinction between the male brain and the female brain. The controversy is primarily rooted not in disagreement about first-order empirical results, but in conflicting assumptions about what is needed to establish a difference between two brain types. Understanding these commitments through the lens of philosophical work on natural kinds, I show that both sides of the debate adopt implausible assumptions about kindhood. Relying instead on a framework provided by cluster theories of natural kinds, I provide a philosophically- and empirically-motivated argument against the view that male and female brains are natural kinds. Because machine learning methods have played an increasingly important role in this debate, my argument has broader implications for the use of machine learning in science. I argue that we should not conflate supervised classification with scientific classification. Some unsupervised machine learning methods, however, might be able to help us identify natural kinds when applied with caution.
| Export/Citation: | EndNote | BibTeX | Dublin Core | ASCII/Text Citation (Chicago) | HTML Citation | OpenURL |
| Social Networking: |
Monthly Views for the past 3 years
Monthly Downloads for the past 3 years
Plum Analytics
Actions (login required)
 |
View Item |
![[feed]](http://philsci-archive.pitt.edu/style/images/feed-icon-32x32.png){kind=icon}