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Explaining Machine Learning Decisions

Published online by Cambridge University Press:  31 January 2022

John Zerilli
John Zerilli*
Affiliation:
University of Oxford, Oxford, UK
*
Emails: john.zerilli@law.ox.ac.uk; john.zerilli@gmail.com
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Abstract

The operations of deep networks are widely acknowledged to be inscrutable. The growing field of Explainable AI (XAI) has emerged in direct response to this problem. However, owing to the nature of the opacity in question, XAI has been forced to prioritise interpretability at the expense of completeness, and even realism, so that its explanations are frequently interpretable without being underpinned by more comprehensive explanations faithful to the way a network computes its predictions. While this has been taken to be a shortcoming of the field of XAI, I argue that it is broadly the right approach to the problem.

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Article
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Philosophy of Science , Volume 89 , Issue 1 , January 2022 , pp. 1 - 19
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of the Philosophy of Science Association

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