On the Complexity of Global Necessary Reasons to Explain Classification

• URL: http://arxiv.org/abs/2501.06766v1
• Date: Sun, 12 Jan 2025 10:25:14 GMT
• Title: On the Complexity of Global Necessary Reasons to Explain Classification
• Authors: Marco Calautti, Enrico Malizia, Cristian Molinaro,
• Abstract summary: 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.<n>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.
• Score: 10.007636884318801
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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