Provably Robust Model-Centric Explanations for Critical Decision-Making

• URL: http://arxiv.org/abs/2110.13937v1
• Date: Tue, 26 Oct 2021 18:05:49 GMT
• Title: Provably Robust Model-Centric Explanations for Critical Decision-Making
• Authors: Cecilia G. Morales, Nicholas Gisolfi, Robert Edman, James K. Miller, Artur Dubrawski
• Abstract summary: We 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.
• Score: 14.367217955827002
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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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