We describe some recent approaches to score-based explanations for query answers in databases and outcomes from classification models in machine learning. The focus is on work done by the author and collaborators. Special emphasis is placed on declarative approaches based on answer-set programming to the use of counterfactual reasoning for score specification and computation. Several examples that illustrate the flexibility of these methods are shown.

翻译：我们描述了最近对数据库中的查询答案和机器学习分类模型结果的评分解释方法,重点是作者和协作者所做的工作,特别强调基于回答组合的宣示方法,在计分规格和计算时采用反事实推理,并举例说明了这些方法的灵活性。