Explainable AI encompasses techniques that make AI model decisions understandable to humans, addressing the black-box nature of deep neural networks. Methods include SHAP values, LIME, attention visualization, and concept-based explanations. XAI is essential for regulated industries: a Moroccan bank must be able to explain why a loan was denied, and a hospital must justify a diagnostic AI's recommendation to a physician.