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Philosophy of science at sea: Clarifying the interpretability of machine learning
Philosophy Compass 17 (6):e12830 (2022)
Abstract
Philosophy Compass, Volume 17, Issue 6, June 2022.Author Profiles
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Citations of this work
Reliability in Machine Learning.Thomas Grote, Konstantin Genin & Emily Sullivan - 2024 - Philosophy Compass 19 (5):e12974.
Understanding with Toy Surrogate Models in Machine Learning.Andrés Páez - 2024 - Minds and Machines 34 (4):45.
We Have No Satisfactory Social Epistemology of AI-Based Science.Inkeri Koskinen - 2024 - Social Epistemology 38 (4):458-475.
ML interpretability: Simple isn't easy.Tim Räz - 2024 - Studies in History and Philosophy of Science Part A 103 (C):159-167.
References found in this work
Extending Ourselves: Computational Science, Empiricism, and Scientific Method.Paul Humphreys - 2004 - New York, US: Oxford University Press.
Understanding from Machine Learning Models.Emily Sullivan - 2022 - British Journal for the Philosophy of Science 73 (1):109-133.
Studies in the logic of explanation.Carl Gustav Hempel & Paul Oppenheim - 1948 - Philosophy of Science 15 (2):135-175.
Explanation and scientific understanding.Michael Friedman - 1974 - Journal of Philosophy 71 (1):5-19.
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