Machine learning and theory-ladenness: a phenomenological account

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 .