Explainable Artificial Intelligence (XAI) is a modern research direction in AI that aims to make machine learning models more transparent and understandable to humans. With the rapid advancement of deep learning techniques, many AI systems are often described as “black boxes” due to the difficulty of interpreting how they reach their decisions. XAI seeks to address this issue by providing clear and interpretable explanations for model outputs. This field focuses on developing methods and tools that help users and researchers understand the reasoning behind predictions or decisions. Common approaches include using simplified surrogate models and feature importance analysis to identify the most influential factors in decision-making. Techniques such as LIME and SHAP are widely used to interpret complex model outputs. Explainable AI is particularly important in sensitive domains such as healthcare, finance, and legal systems, where decisions require transparency and accountability. For example, when AI systems are used for medical diagnosis or loan approval, it is crucial to explain why a particular decision was made to ensure fairness and reduce bias.