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  1. Understanding From Machine Learning Models.Emily Sullivan - forthcoming - British Journal for the Philosophy of Science:axz035.
    Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal models provide understanding misguided? In (...)
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  • Understanding the Progress of Science.C. D. McCoy - manuscript
    This paper develops a problem-solving account of scientific progress that takes understanding as the principal epistemic aim of science. It examines a recent paper of Bird's on scientific progress, argues for the primacy of understanding over knowledge in this context, illustrates the account using a Kuhnian picture of science, and defends it against knowledge reductionism.
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  • Is Knowledge of Causes Sufficient for Understanding?Xingming Hu - 2018 - Canadian Journal of Philosophy 49 (3):291-313.
    ABSTRACT: According to a traditional account, understanding why X occurred is equivalent to knowing that X was caused by Y. This paper defends the account against a major objection, viz., knowing-that is not sufficient for understanding-why, for understanding-why requires a kind of grasp while knowledge-that does not. I discuss two accounts of grasp in recent literature and argue that if either is true, then knowing that X was caused by Y entails at least a rudimentary understanding of why X occurred. (...)
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