Machine Learning and Theory Ladenness -- A Phenomenological Account
This addresses a philosophical issue in ML for science, shifting debates from descriptive to normative priorities, but it is incremental as it builds on existing trends in philosophy of science.
The paper tackles the problem of theory ladenness in machine learning applied to science, arguing that ML model-building is largely indifferent to domain theory, though it remains weakly theory-laden as 'theory infection', with implications for cross-disciplinary transferability.
We provide an analysis of theory ladenness in machine learning 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.