Explainability in AI is essential for building trust, ensuring compliance with regulations, optimizing performance, and engaging stakeholders. It involves methods and tools designed to make AI models transparent, interpretable, and understandable. Explainability promotes trust by helping users understand how decisions are made, ensures compliance with legal and ethical standards, makes it easier to identify errors and biases, and encourages adoption in high-stakes environments. XAI uses techniques like post-hoc analysis, model simplification, and visualization tools to clarify predictions and explain why a particular decision was made. It is necessary for ensuring ethical AI usage, preventing data misuse, improving decision-making, and meeting regulatory requirements. Global explanations provide an overview of how the model behaves and makes decisions, while local explanations focus on specific predictions and explain why a particular decision was made. XAI is a key component of Responsible AI, ensuring fairness, accountability, and transparency. Even high-performing models can make biased or unethical decisions without explainability, highlighting the importance of this approach in ensuring models are not only accurate but also trustworthy and ethically aligned. However, XAI faces challenges like explaining the complexity of deep neural networks, balancing transparency with accuracy, and the resource intensity of explaining large datasets. While it cannot eliminate bias entirely, XAI helps identify and reduce it by addressing bias through careful data curation, ethical practices, and continuous monitoring.