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arXiv · 2110.13937

Provably Robust Model-Centric Explanations for Critical Decision-Making

Cecilia G. Morales·Nicholas Gisolfi·Robert Edman·James K. Miller·Artur Dubrawski
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Abstract

We recommend using a model-centric, Boolean Satisfiability (SAT) formalism to obtain useful explanations of trained model behavior, different and complementary to what can be gleaned from LIME and SHAP, popular data-centric explanation tools in Artificial Intelligence (AI). We compare and contrast these methods, and show that data-centric methods may yield brittle explanations of limited practical utility. The model-centric framework, however, can offer actionable insights into risks of using AI models in practice. For critical applications of AI, split-second decision making is best informed by robust explanations that are invariant to properties of data, the capability offered by model-centric frameworks.

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Cecilia G. Morales, Nicholas Gisolfi, Robert Edman, James K. Miller, Artur Dubrawski. 2021-10-26. Provably Robust Model-Centric Explanations for Critical Decision-Making. https://arxiv.org/abs/2110.13937

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