Machine Learning and Theory Ladenness -- A Phenomenological Account
<|reference_start|>Machine Learning and Theory Ladenness -- A Phenomenological Account: In recent years, the dissemination of machine learning (ML) methodologies in scientific research has prompted discussions on theory ladenness. More specifically, the issue of theory ladenness has remerged as questions about whether and how ML models (MLMs) and ML modelling strategies are impacted by the domain theory of the scientific field in which ML is used and implemented (e.g., physics, chemistry, biology, etc). On the one hand, some have argued that there is no difference between traditional (pre ML) and ML assisted science. In both cases, theory plays an essential and unavoidable role in the analysis of phenomena and the construction and use of models. Others have argued instead that ML methodologies and models are theory independent and, in some cases, even theory free. In this article, we argue that both positions are overly simplistic and do not advance our understanding of the interplay between ML methods and domain theories. Specifically, we provide an analysis of theory ladenness in ML assisted science. Our analysis reveals that, while the construction of MLMs can be relatively independent of domain theory, the practical implementation and interpretation of these models within a given specific domain still relies on fundamental theoretical assumptions and background knowledge.<|reference_end|>
@article{termine2024machine,
    title={Machine Learning and Theory Ladenness -- A Phenomenological Account},
    author={Alberto Termine, Emanuele Ratti, Alessandro Facchini},
    journal={arXiv preprint arXiv:2409.11277},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.11277},
    primaryClass={cs.AI}
}