In this expository article we highlight the relevance of explanations for artificial intelligence, in general, and for the newer developments in {\em explainable AI}, referring to origins and connections of and among different approaches. We describe in simple terms, explanations in data management and machine learning that are based on attribution-scores, and counterfactuals as found in the area of causality. We elaborate on the importance of logical reasoning when dealing with counterfactuals, and their use for score computation.
翻译:在本解释性文章中,我们强调对人工智能的一般解释和对可解释的人工智能的最新发展的解释的相关性,并提到不同方法的起源和联系以及不同方法之间的联系。我们简单描述数据管理和机器学习的解释,这些解释以归属分数和因果关系领域发现的反事实为依据。我们阐述了处理反事实时逻辑推理的重要性,以及它们用于计分的情况。</s>