发表机构
LISIC, ULCO（滨海大学LISIC实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对布尔分类器的溯因解释在有序二元决策图上仍难计算的问题，提出用分类器的双轨编码的适当表示来高效计算这类困难的XAI查询。
AI 中文摘要
人工智能（AI）在实际应用中的广泛应用引发了人们对其可信度的诸多担忧，尤其是在关键应用领域。可解释人工智能（XAI）领域应运而生，旨在向用户解释AI系统做出的决策。文献中已针对布尔分类器提出了多种解释，包括溯因解释和对比解释，每种解释都能为分类器的决策提供不同视角。然而，一般而言，计算布尔分类器决策的解释是一个困难问题。应对这种复杂性的一种方法是依赖分类器的编译表示，对于该表示，每个解释都可以高效计算。遗憾的是，本文证明，即使对于知识编译图中最易处理的子集之一——有序二元决策图，几类溯因解释仍然难以计算，这类解释包括较短的溯因解释或包含解释对象偏好的溯因解释。为了恢复使用编译表示的优势，本文表明，分类器的双轨编码的适当表示可用于高效计算这些类别的解释。
英文摘要
The widespread adoption of artificial intelligence (AI) within real-world applications has raised a lot of concerns regarding their trustworthiness, especially in critical applications. The field of eXplainable AI (XAI) has emerged with the objective of providing explanations to the users about the decisions made by AI systems. Several explanations for boolean classifiers have been introduced in the literature, including abductive and contrastive explanations, each giving a different insight on the decision of the classifier. However, computing an explanation for a decision of a boolean classifier is a hard problem in general. One way to deal with this complexity is to rely on a compiled representation of the classifier for which each explanation can be computed efficiently. Unfortunately, we prove in this paper that several classes of abductive explanations, remain hard to compute even for Ordered Binary Decision Diagrams, one of the most tractable subsets of the knowledge compilation map. Included in such classes are shorter abductive explanations or abductive explanations that include the explainee's preferences. To recover the benefits of working with compiled representations, we show that a proper representation of the dual-rail encoding of the classifier can be used to compute efficiently these classes of explanations.
Comments20 pages, 2 figures, full version of a submitted conference paper with detailed proofs
