Explainable AI (XAI) is a field dedicated to making AI models and their decisions more transparent and interpretable. As AI systems increasingly influence critical areas like healthcare, finance, and criminal justice, understanding how these models arrive at their conclusions becomes essential for building trust, ensuring accountability, and making informed decisions. This module explores the concept of XAI, its significance, and the tools and methods used to make AI models more understandable. 

Explainable AI refers to methods and techniques that make AI models’ predictions and decisions understandable to humans. Many AI models, especially complex ones like deep neural networks, are considered “black boxes” because their decision-making processes are difficult to interpret. XAI aims to break open these black boxes, allowing users to understand and trust how AI models operate. Key terms: 

• Interpretability: The degree to which a human can understand the cause of a decision. 
• Transparency: How openly and clearly an AI model’s processes can be examined and explained.