Explainable AI focuses on making machine learning models more transparent and interpretable to human users. Black-box models like deep neural networks often lack interpretability, creating trust and accountability issues. Techniques include feature importance analysis, LIME (Local Interpretable Model-agnostic Explanations), and SHAP (SHapley Additive exPlanations). Interpretability is crucial for high-stakes applications like healthcare, finance, and criminal justice where decisions require justification.