  The rapidly advancing domain of Explainable Artificial Intelligence (XAI) has
sparked significant interests in developing techniques to make AI systems more
transparent and understandable. Nevertheless, in real-world contexts, the
methods of explainability and their evaluation strategies present numerous
limitations.Moreover, the scope of responsible AI extends beyond just
explainability. In this paper, we explore these limitations and discuss their
implications in a boarder context of responsible AI when considering other
important aspects, including privacy, fairness and contestability.
