Making AI Understandable: A Beginner’s Guide to Explainable AI (XAI)

Explainable AI (XAI) aims to make AI decisions transparent and trustworthy by addressing the 'black box' problem of complex models. Two main approaches exist: intrinsically interpretable models (decision trees, linear regression) and post-hoc tools like LIME, SHAP, and saliency maps for complex models. XAI is critical for trust, debugging, regulatory compliance, and ethical AI across domains such as healthcare, finance, and autonomous vehicles.