研究分类器全局解释的最小必要条件复杂度，揭示其理论边界。
On the Complexity of Global Necessary Reasons to Explain Classification
- 从最小必要条件出发，构建分类器全局解释的新框架。
- 证明多种常见分类器在该解释任务中为高复杂度问题。
- 适合对可解释性理论感兴趣的科研人员参考。
可解释人工智能近年来受到广泛关注，理解AI系统决策背后的原因对其成功应用至关重要。解释分类器行为是其中一个重要问题。该领域已有局部解释与全局解释两种范式，前者关注特定实例的决策原因，后者则试图解释分类器整体行为，不依赖于具体实例。本文聚焦全局解释，以「通用实例被赋予特定类别的最小必要条件」来解释分类结果。我们对自然最小性准则及文献中重要分类器家族进行了系统的复杂性分析，揭示了该问题在不同场景下的计算难度边界。
原文摘要 · Abstract (English)
Explainable AI has garnered considerable attention in recent years, as understanding the reasons behind decisions or predictions made by AI systems is crucial for their successful adoption. Explaining classifiers' behavior is one prominent problem. Work in this area has proposed notions of both local and global explanations, where the former are concerned with explaining a classifier's behavior for a specific instance, while the latter are concerned with explaining the overall classifier's behavior regardless of any specific instance. In this paper, we focus on global explanations, and explain classification in terms of ``minimal'' necessary conditions for the classifier to assign a specific class to a generic instance. We carry out a thorough complexity analysis of the problem for natural minimality criteria and important families of classifiers considered in the literature.
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