Ratti, Emanuele and Termine, Alberto and Facchini, Alessandro
(2025)
Machine Learning and Theory-Ladenness: A Phenomenological Account.
[Preprint]
Abstract
We provide an analysis of theory-ladenness in machine learning (ML) in science, where ‘theory’ (that we call 'domain-theory') refers to the domain knowledge of the scientific discipline where ML is used. By constructing an account of ML models based on a comparison with phenomenological models, we show (against recent trends in philosophy of science) that ML model-building is mostly indifferent to domain-theory, even if the model remains theory-laden in a weak sense, which we call theory-infection. These claims, we argue, have far-reaching consequences for the transferability of ML across scientific disciplines, and shift the priorities of the debate on theory-ladenness in ML from descriptive to normative.
Available Versions of this Item
Monthly Views for the past 3 years
Monthly Downloads for the past 3 years
Plum Analytics
Actions (login required)
 |
View Item |
![[img]](https://philsci-archive.pitt.edu/style/images/fileicons/text.png)
![[feed]](http://philsci-archive.pitt.edu/style/images/feed-icon-32x32.png){kind=icon}