Explainable AI (Xai)

Introduces the fundamental concepts of explainable and interpretable AI. Provides a clear distinction between black-box, glass-box, and hybrid AI models. Covers popular XAI techniques, including feature importance, LIME, SHAP, counterfactuals, and surrogate models. Examines applications of XAI across healthcare, finance, agriculture, governance, cybersecurity, and education. Discusses ethical concerns, bias detection, fairness, and accountability in AI systems. Explores the role of XAI in regulatory compliance and trustworthy AI frameworks. Integrates real-world case studies to demonstrate practical implementation. Serves as a valuable resource for students, researchers, policymakers, and industry professionals.