The need for human-centred Explainable Artificial Intelligence (XAI) arises from the increasing complexity and pervasiveness of AI systems in many aspects of daily life and critical decision-making processes. Mining the literature, research shows a lack of human-centred methods applied to the design and evaluation of XAI. In this work, we compile current works in human-centred XAI and present a case study contributing to user studies to assess explanations. The case study aims at evaluating the impact of local and global explanations on human’s trust and understanding of a facial expression recognizer. Results show that explanations are appreciated when present, but when no explanations are given, users apply their own mental model on how the system works and trust it if their experience using it is positive.