Machine learning and theory-ladenness: a phenomenological account
Abstract
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 .
Fields of Study & Subjects
Ethics and Social Impacts of AI
Computational and Text Analysis Methods
Philosophy and History of Science
Publication details
- Journal / source
- Synthese
- Publisher
- Springer Science and Business Media LLC
- Volume
- 207
- Issue
- 3
- Publication date
- 17 February 2026
- Language
- EN