Values and inductive risk in machine learning modelling: the case of binary classification models

European Journal for Philosophy of Science 11 (4):1-27 (2021)
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I examine the construction and evaluation of machine learning binary classification models. These models are increasingly used for societal applications such as classifying patients into two categories according to the presence or absence of a certain disease like cancer and heart disease. I argue that the construction of ML classification models involves an optimisation process aiming at the minimization of the inductive risk associated with the intended uses of these models. I also argue that the construction of these models is underdetermined by the available data, and that this makes it necessary for ML modellers to make social value judgments in determining the error costs used in ML optimization. I thus suggest that the assessment of the inductive risk with respect to the social values of the intended users is an integral part of the construction and evaluation of ML classification models. I also discuss the implications of this conclusion for the philosophical debate concerning inductive risk.



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Understanding from Machine Learning Models.Emily Sullivan - 2022 - British Journal for the Philosophy of Science 73 (1):109-133.
Transparency in Complex Computational Systems.Kathleen A. Creel - 2020 - Philosophy of Science 87 (4):568-589.
The Scientist Qua Scientist Makes Value Judgments.Richard Rudner - 1953 - Philosophy of Science 20 (1):1-6.
Inductive risk and values in science.Heather Douglas - 2000 - Philosophy of Science 67 (4):559-579.
Deep learning: A philosophical introduction.Cameron Buckner - 2019 - Philosophy Compass 14 (10):e12625.

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