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arXiv · 2306.06134

Sound Explanation for Trustworthy Machine Learning

Kai Jia·Pasapol Saowakon·Limor Appelbaum·Martin Rinard
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Abstract

We take a formal approach to the explainability problem of machine learning systems. We argue against the practice of interpreting black-box models via attributing scores to input components due to inherently conflicting goals of attribution-based interpretation. We prove that no attribution algorithm satisfies specificity, additivity, completeness, and baseline invariance. We then formalize the concept, sound explanation, that has been informally adopted in prior work. A sound explanation entails providing sufficient information to causally explain the predictions made by a system. Finally, we present the application of feature selection as a sound explanation for cancer prediction models to cultivate trust among clinicians.

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Kai Jia, Pasapol Saowakon, Limor Appelbaum, Martin Rinard. 2023-06-08. Sound Explanation for Trustworthy Machine Learning. https://arxiv.org/abs/2306.06134

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