Sound Explanation for Trustworthy Machine Learning

• URL: http://arxiv.org/abs/2306.06134v1
• Date: Thu, 8 Jun 2023 19:58:30 GMT
• Title: Sound Explanation for Trustworthy Machine Learning
• Authors: Kai Jia, Pasapol Saowakon, Limor Appelbaum, Martin Rinard
• Abstract summary: We argue against the practice of interpreting black-box models via attributing scores to input components. We then formalize the concept of sound explanation, that has been informally adopted in prior work. We present the application of feature selection as a sound explanation for cancer prediction models to cultivate trust among clinicians.
• Score: 11.779125616468194
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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