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Overview of Explainable AI (XAI), a field dedicated to making AI systems more transparent, interpretable, and trustworthy. It begins by defining the "black box" problem in AI, distinguishing between systems that are intentionally opaque (e.g., proprietary algorithms) and those that become so due to their inherent complexity (e.g., deep neural networks). The document then details the multifaceted objectives of XAI, which include fostering trust, ensuring accountability, enabling auditability, promoting fairness, improving models, and empowering users. It further categorizes various XAI methodologies into intrinsic (white box models like decision trees) and post-hoc techniques (like LIME and SHAP), which are applied after a model is trained. Finally, the text explores XAI's critical applications across high-stakes domains such as healthcare, finance, and autonomous systems, highlighting its role in mitigating risks, addressing regulatory demands, and navigating the evolving ethical and legal landscape.