AI systems are increasingly being used to drive high-stakes decision-making in domains such as medical diagnosis, credit approval, and human resources. Ensuring that AI-driven decisions are properly justified is crucial. Justification implies that decisions align with relevant norms. However, the opacity of many AI systems hinders this kind of justification since it makes it difficult to determine which features influence these systems’ outputs and whether they have learned to track spurious correlations rather than relevant patterns. Although explainable AI (XAI) offers potential solutions by uncovering the reasons for AI systems’ outputs, its role in justification remains underexplored. This chapter adopts a framework from philosophy of action to differentiate between the different types of reasons that may be given for an AI system’s outputs, assess how these types of reasons contribute to justification, and evaluate the capacity of current XAI methods to identify and describe such reasons. Overall, while the chapter shows how XAI methods could be used to identify reasons with which to justify AI-driven decisions, it also highlights some of these methods’ current limitations.