Locally-Minimal Probabilistic Explanations

TL;DR

This paper introduces efficient algorithms for computing locally-minimal probabilistic abductive explanations, providing rigorous, high-quality approximations crucial for trustworthy AI in high-stakes domains.

Contribution

It presents novel algorithms that efficiently approximate probabilistic abductive explanations, addressing computational challenges and enhancing explainability in critical AI applications.

Findings

Algorithms are practically efficient for real-world use.

Locally-minimal explanations improve interpretability without excessive complexity.

Experimental results validate the effectiveness of the proposed methods.

Abstract

Explainable Artificial Intelligence (XAI) is widely regarding as a cornerstone of trustworthy AI. Unfortunately, most work on XAI offers no guarantees of rigor. In high-stakes domains, e.g. uses of AI that impact humans, the lack of rigor of explanations can have disastrous consequences. Formal abductive explanations offer crucial guarantees of rigor and so are of interest in high-stakes uses of machine learning (ML). One drawback of abductive explanations is explanation size, justified by the cognitive limits of human decision-makers. Probabilistic abductive explanations (PAXps) address this limitation, but their theoretical and practical complexity makes their exact computation most often unrealistic. This paper proposes novel efficient algorithms for the computation of locally-minimal PXAps, which offer high-quality approximations of PXAps in practice. The experimental results demonstrate the practical efficiency of the proposed algorithms.