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<p>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 <i>indifferent</i> to domain-theory, even if the model remains theory-laden in a weak sense, which we call <i>theory-infection</i>. These claims, we argue, have far-reaching consequences for the <i>transferability</i> of ML across scientific disciplines, and shift the priorities of the debate on theory-ladenness in ML from <i>descriptive</i> to <i>normative</i>.</p>

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Machine learning and theory-ladenness: a phenomenological account

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月17日

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  • Alberto Termine,
  • Emanuele Ratti,
  • Alessandro Facchini

摘要

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.