Machine learning (ML) methodology, fueled with access to ever-increasing masses of data and unprecedented computing power, has been the main driving factor of recent progress in artificial intelligence (AI) and its applications in various branches of science and technology, industry and business, economics and finance, amongst others. In this regard, ML is most commonly perceived as a means for predictive modeling, that is, for the data-driven construction of a model that is mainly used for the purpose of predicting unknown facts in a specific context -- albeit models may, of course, serve other purposes, too, such as understanding and explanation, or may have a more descriptive flavor. A predictive model, or "predictor" in ML jargon, is trained in a supervised manner on cases encountered by the "learner" over the course of time, such as emails categorized as spam or non-spam, and the model is then used to make predictions in future situations, e.g., to automatically mark new emails. Looking at emerging applications of ML methodology, there is a visible shift from predictive modeling to prescriptive modeling, by which we mean the task of learning a model that stipulates appropriate decisions or actions to be taken in real-world scenarios. In fact, decisions are nowadays increasingly automated and made by algorithms instead of humans, and most of these automated decision making (ADM) algorithms are trained on data using ML methods. For example, think of decisions in the context of employees recruitment, such as hiring or placement decisions [41].