Explainable AI (XAI) comprises techniques and methods designed to make AI model decisions transparent and interpretable to humans. As machine learning models—particularly deep neural networks—grow increasingly complex, their internal decision-making processes often become opaque "black boxes." XAI addresses this challenge by providing tools to understand which features drive predictions, how models arrive at specific outputs, and whether model behavior aligns with human intuition and fairness principles. Understanding interpretability is critical not only for debugging and improving models but also for building trust, ensuring regulatory compliance (such as GDPR's "right to explanation" or the EU AI Act), and detecting biases in high-stakes domains like healthcare, finance, and criminal justice.