Provably Robust Model-Centric Explanations for Critical Decision-Making. (arXiv:2110.13937v1 [cs.LG])

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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