Andrews, Mel (2023) The Devil in the Data: Machine Learning & the Theory-Free Ideal. [Preprint]

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Abstract

Philosophers of science have argued that the widespread adoption of the methods of machine learning (ML) will entail radical changes to the variety of epistemic outputs science is capable of producing. Call this the disruption claim. This, in turn, rests on a distinctness claim, which holds ML to exist on novel epistemic footing relative to classical modelling approaches in virtue of its atheoreticity. We describe the operation of ML systems in scientific practice and reveal it to be a necessarily theory-laden exercise. This undercuts claims of epistemic distinctness and, therefore, at least one path to claims of disruption.

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Item Type:	Preprint
Creators:	Creators Email ORCID Andrews, Mel melandre@andrew.cmu.edu 0000-0002-0042-5098
Keywords:	machine learning, formal methods, mathematical modelling, scientific theories, data
Subjects:	General Issues > Data Specific Sciences > Computer Science Specific Sciences > Artificial Intelligence > Machine Learning General Issues > Theory/Observation
Depositing User:	Mel Andrews
Date Deposited:	26 Oct 2023 02:15
Last Modified:	26 Oct 2023 02:15
Item ID:	22690
Subjects:	General Issues > Data Specific Sciences > Computer Science Specific Sciences > Artificial Intelligence > Machine Learning General Issues > Theory/Observation
Date:	23 October 2023
URI:	https://philsci-archive.pitt.edu/id/eprint/22690

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