We describe recent research on the use of actual causality in the definition of responsibility scores as explanations for query answers in databases, and for outcomes from classification models in machine learning. In the case of databases, useful connections with database repairs are illustrated and exploited. Repairs are also used to give a quantitative measure of the consistency of a database. For classification models, the responsibility score is properly extended and illustrated. The efficient computation of Shap-score is also analyzed and discussed. The emphasis is placed on work done by the author and collaborators.

翻译：本文描述了近期关于实际因果性在定义责任得分方面的研究，此类责任得分被用作数据库查询答案及机器学习分类模型结果的可解释性依据。在数据库场景中，本文阐明并利用了与数据库修复的有用关联。修复方法还被用于对数据库一致性进行量化度量。针对分类模型，本文对责任得分进行了合理扩展与阐释。同时，本文分析并讨论了Shap得分的高效计算方法。研究重点为作者及其合作者的工作成果。