Explainable AI (XAI)

Explainable AI (XAI) focuses on developing AI models that can make their decision-making processes and output results understandable to humans. As AI systems become increasingly complex (especially deep learning models), understanding why they make specific predictions or decisions becomes crucial, particularly in high-risk domains like healthcare and finance. XAI aims to improve AI transparency, trustworthiness, and fairness. Techniques include attention mechanisms that show which parts of input data the model focuses on, LIME (Local Interpretable Model-agnostic Explanations) for local explanations, and SHAP (SHapley Additive exPlanations) for feature importance. The goal is to build AI systems that humans can understand, trust, and safely deploy in critical applications.