Synthese (1-2):1-21 (2020)

Vincent Lam
University of Bern
Julie Jebeile
University of Bern
Machine learning methods have recently created high expectations in the climate modelling context in view of addressing climate change, but they are often considered as non-physics-based ‘black boxes’ that may not provide any understanding. However, in many ways, understanding seems indispensable to appropriately evaluate climate models and to build confidence in climate projections. Relying on two case studies, we compare how machine learning and standard statistical techniques affect our ability to understand the climate system. For that purpose, we put five evaluative criteria of understanding to work: intelligibility, representational accuracy, empirical accuracy, coherence with background knowledge, and assessment of the domain of validity. We argue that the two families of methods are part of the same continuum where these various criteria of understanding come in degrees, and that therefore machine learning methods do not necessarily constitute a radical departure from standard statistical tools, as far as understanding is concerned.
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DOI 10.1007/s11229-020-02865-z
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References found in this work BETA

Studies in the Logic of Explanation.Carl Gustav Hempel & Paul Oppenheim - 1948 - Philosophy of Science 15 (2):135-175.
Studies in the Logic of Explanation.Carl G. Hempel & Paul Oppenheim - 1948 - Journal of Symbolic Logic 14 (2):133-133.
Understanding From Machine Learning Models.Emily Sullivan - 2022 - British Journal for the Philosophy of Science 73 (1):109-133.

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Citations of this work BETA

Value Management and Model Pluralism in Climate Science.Julie Jebeile & Michel Crucifix - 2021 - Studies in History and Philosophy of Science Part A 88 (August 2021):120-127.

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