<ul><li>Explainable artificial intelligence (XAI) aims to help human decision-makers understand complex machine learning models.</li><li>Logic-based XAI offers rigorous explanations but can be complex, especially for highly complex ML models.</li><li>Recent work proposes distance-restricted explanations that are rigorous within a small input distance.</li><li>This paper investigates algorithms to scale up logic-based explainers for computing explanations with a large number of inputs.</li></ul>

Distance-Restricted Explanations: Theoretical Underpinnings & Efficient Implementation

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