Actionable Model-Centric Explanations (Student 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.

Authors

• Cecilia G. Morales
• Nicholas Gisolfi Carnegie Mellon University
• Robert Edman
• James K. Milller
• Artur Dubrawski Auton Lab, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA

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