Logic for Explainable AI

Explainability requires multiple perspectives: Understanding AI decisions isn't one-dimensional. The paper identifies three critical angles: identifying the reasons for a decision, determining what information is irrelevant to it, and finding minimal changes that would produce different outcomes. A complete explanation needs all three perspectives.

Logic-based approaches can work across different AI systems: Rather than requiring custom explanations for each type of AI model, the paper demonstrates that a unified logical framework can be applied to diverse classifiers—from decision trees and random forests to neural networks and Bayesian networks—suggesting a more standardized path toward explainability.

"Minimal" explanations are more useful than complete ones: The paper emphasizes identifying minimal sufficient conditions (the smallest amount of information needed to justify a decision) and minimal necessary conditions (the smallest changes needed to alter a decision). This focus on minimalism makes explanations more actionable and easier for humans to understand and act upon.

Symbolic logic provides a rigorous foundation for explainability: By grounding explainability in formal logic rather than ad-hoc methods, the paper offers both semantic clarity (what explanations actually mean) and computational tractability (how to generate them efficiently), which is essential for building trustworthy AI systems.