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. This claim, we argue, has far-reaching consequences for the transferability of ML across scientific disciplines, and shifts the priorities of the debate on theory-ladenness in ML from descriptive to normative.
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