The last decade has witnessed the rise of a black box society where obscure classification models are adopted by Artificial Intelligence systems (AI). The lack of explanations of how AI systems make decisions is a key ethical issue to their adoption in socially sensitive and safety-critical contexts. Indeed, the problem is not only for lack of transparency but also for possible biases inherited by the AI from prejudices hidden in the training data. Thus, the research in eXplainable AI (XAI) has recently caught much attention. The applications in which AI systems are employed are various. Therefore, there are many requirements for different types of explanations for different users. We survey the existing proposals in the literature by discussing which are the principles of XAI. In addition, we illustrate different types of explanations returned by established explainers. Finally, we discuss their usability and how they can be exploited in real-world applications.