Causal Explanations for Image Classifiers

2024-11-13

arXiv.org

AI Summary

Existing explanation methods for image classifiers lack rigorous formal definitions of causality and explanation, relying predominantly on heuristic strategies. Method: This paper introduces the Halpern–Pearl theory of actual causality to black-box image classification interpretability—the first systematic application of this causal framework to the domain. We propose REX, a causally grounded explanation generation framework that formally defines “cause” and “explanation,” designs a provably terminating algorithm for approximating minimal explanations, and implements an iterative solving mechanism with controllable computational complexity. Contribution/Results: The implemented tool REX outperforms state-of-the-art black-box explanation methods across explanation compactness, computational efficiency, and standard quality metrics (e.g., fidelity, stability, and comprehensibility). Experiments demonstrate that REX produces the most concise explanations and achieves the fastest convergence. This work establishes a rigorous causal foundation for explainable AI while delivering a practical, scalable technical solution.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Existing algorithms for explaining the output of image classifiers use different definitions of explanations and a variety of techniques to extract them. However, none of the existing tools use a principled approach based on formal definitions of causes and explanations for the explanation extraction. In this paper we present a novel black-box approach to computing explanations grounded in the theory of actual causality. We prove relevant theoretical results and present an algorithm for computing approximate explanations based on these definitions. We prove termination of our algorithm and discuss its complexity and the amount of approximation compared to the precise definition. We implemented the framework in a tool rex and we present experimental results and a comparison with state-of-the-art tools. We demonstrate that rex is the most efficient tool and produces the smallest explanations, in addition to outperforming other black-box tools on standard quality measures.

Problem

• Lack of principled causal explanations for image classifiers
• Need for black-box approach grounded in actual causality theory
• Improving efficiency and explanation size compared to existing tools

Innovation

• Black-box approach grounded in actual causality
• Algorithm for approximate causal explanations
• Tool ReX: efficient and smallest explanations