Philosophy of Science:1-13 (forthcoming)

Authors
Emily Sullivan
Eindhoven University of Technology
Abstract
Under what conditions does machine learning (ML) model opacity inhibit the possibility of explaining and understanding phenomena? In this paper, I argue that non-epistemic values give shape to the ML opacity problem even if we keep researcher interests fixed. Treating ML models as an instance of doing model-based science to explain and understand phenomena reveals that there is (i) an external opacity problem, where the presence of inductive risk imposes higher standards on externally validating models, and (ii) an internal opacity problem, where greater inductive risk demands a higher level of transparency regarding the inferences the model makes.
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DOI 10.1017/psa.2022.62
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References found in this work BETA

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.
Understanding From Machine Learning Models.Emily Sullivan - 2022 - British Journal for the Philosophy of Science 73 (1):109-133.

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