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<p>This paper contends with the notion that the methods of machine learning (ML) are unique among the tools of science in enabling a form of theory-free inductive inference. I challenge these assertions of epistemic distinctness, attributing the prevalence of these views to an untenable conception of scientific objectivity: what I term a <i>theory-free ideal</i>, in homage to its normative counterpart. ML, as a formal method of induction, must rely on conceptual or theoretical resources to get inference off the ground. By means of two case studies, I argue that this theory-free ideal has a deleterious effect on the epistemic standing of ML-involving science.</p>

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The Immortal Science of ML: Machine Learning and the Theory-Free Ideal

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  • 出版模式：

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  • 发表日期：

    2025年9月25日

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  • Mel Andrews

摘要

This paper contends with the notion that the methods of machine learning (ML) are unique among the tools of science in enabling a form of theory-free inductive inference. I challenge these assertions of epistemic distinctness, attributing the prevalence of these views to an untenable conception of scientific objectivity: what I term a theory-free ideal, in homage to its normative counterpart. ML, as a formal method of induction, must rely on conceptual or theoretical resources to get inference off the ground. By means of two case studies, I argue that this theory-free ideal has a deleterious effect on the epistemic standing of ML-involving science.