EXPLAINABLE ARTIFICIAL INTELLIGENCE

Explainable Artificial Intelligence (XAI) has emerged as an important area of Artificial Intelligence that addresses the limitations of complex and often opaque AI models. Modern machine learning and deep learning systems can achieve high levels of accuracy in tasks such as prediction, classification, diagnosis, and decision-making; however, their decisions are often difficult for users to understand. This chapter provides an overview of Explainable AI and its role in making AI systems more transparent, trustworthy, accountable, and understandable. It discusses the importance of explainability in areas such as transparency and trust, accountability, regulatory compliance, bias detection, fairness, and model improvement. Different types of explainability, including global, local, intrinsic, and post-hoc explainability, are examined. The chapter also presents important XAI techniques such as feature importance, Local Interpretable Model-Agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), saliency maps, and rule-based explanations. Applications of XAI in healthcare, finance, autonomous vehicles, legal systems, and education are discussed to demonstrate its practical significance. The chapter further highlights major challenges, including the trade-off between accuracy and interpretability, model complexity, lack of standardization, human understanding, and computational cost. Finally, future directions such as human-centred AI, interactive explanations, standard evaluation metrics, and ethical integration are discussed. XAI is therefore an essential component of responsible AI, supporting greater trust, fairness, transparency, and reliability in intelligent systems.