Artificial Intelligence (AI) systems have grown commonplace in modern life, with various applications from customized suggestions to self-driving vehicles. As these systems get more complicated, the necessity for transparency in their decision-making processes becomes more critical. Explainability refers to an AI system’s ability to explain how and why it made a certain judgement or prediction. Recently, there has been a surge of interest in constructing explainable AI (XAI) systems that give insights into the decision-making processes of machine learning models. This paper discloses and elaborates upon a selection of XAI techniques, identifies current challenges and possible future directions in XAI research. --- Disclaimer: This is a preprint version of the article. The content here is for view-only purposes. This is not the final published version and may differ from the version of record. Please refer to the official version for citation and authoritative use.