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.

翻译：本文分析了机器学习在科学应用中的理论负载性，其中"理论"（我们称之为"领域理论"）指代机器学习应用学科的专业领域知识。通过构建基于现象学模型对比的机器学习模型阐释框架，我们证明——与近期科学哲学研究趋势相反——机器学习建模过程基本独立于领域理论，即使模型仍以我们称为"理论感染"的弱形式保持理论负载性。我们认为，这些论断对机器学习跨学科迁移具有深远影响，并将机器学习理论负载性的讨论重点从描述性转向规范性层面。