XAI, or Explainable Artificial Intelligence, is an area of AI research focused on developing models that provide clear and understandable explanations for their predictions or decisions. As AI systems become more complex, the need for transparency increases, especially in critical applications like healthcare, finance, and autonomous driving. XAI techniques aim to bridge the gap between human interpretability and machine learning models by using methods such as feature importance, decision trees, and rule-based explanations. By making AI systems more interpretable, XAI helps users trust and effectively interact with these technologies, ensuring responsible AI deployment.