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：在这篇阐述性文章中，我们强调了解释在人工智能中的普遍相关性，特别是对于可解释人工智能领域的新进展，追溯了不同方法的起源与相互联系。我们用简单的术语描述了数据管理与机器学习中基于归因分数的解释，以及因果关系领域中出现的反事实解释。我们详细阐述了在处理反事实时逻辑推理的重要性，及其在分数计算中的应用。