Explainable AI (XAI) refers to methods and techniques in artificial intelligence (especially in machine learning and deep learning) that make the decisions and internal logic of AI systems understandable to humans . As AI systems are increasingly used in high-stakes domains —such as healthcare, finance, law, and autonomous vehicles— transparency, trust, and accountability become crucial. 🧠 What Is Explainable AI? Explainable AI (XAI) answers the question: "Why did the AI make that decision?" It focuses on making black-box models (like deep neural networks) more interpretable to: Developers (debugging and optimization) End users (trust and adoption) Regulators and auditors (compliance and fairness) 🔍 Why Is Explainability Important? Area Reason ⚖️ Ethical AI Prevent hidden bias and ensure fair outcomes 🛡️ Safety & Reliability Understand AI failures and avoid critical errors 📜 Regulatory Compliance Required by GDPR, EU AI Act, and other laws 🔬 S...