Explainable AI (XAI) refers to methods and techniques that make the behavior and decisions of AI systems understandable to humans. The goal is to provide transparency by having AI systems explain their purpose, rationale, and decision-making process in human-understandable terms​Unlike “black box” models that offer great predictive power with opaque inner workings (e.g., deep neural networks), XAI aims to illuminate how inputs are transformed into outputs, which features influenced a decision, and why the model behaved a certain way. This can involve simple interpretable models, visualizations of learned features, or post-hoc explanation tools (like SHAP or LIME for feature importance). XAI is crucial for building trust in AI systems, debugging models, ensuring fairness (by detecting bias in decisions), and meeting regulatory requirements for explainability​In summary, XAI seeks to bridge the gap between complex AI models and human comprehension by providing clear explanations of AI behavior.