The societal and economic significance of machine learning (ML) cannot be overstated, with many remarkable advances made in recent years. However, the operation of complex ML models is most often inscrutable, with the consequence that decisions taken by ML models cannot be fathomed by human decision makers. It is therefore of importance to devise automated approaches to explain the predictions made by complex ML models. This is the main motivation for eXplainable AI (XAI). Explanations thus serv...