Explainable AI (XAI) focuses on providing transparency and interpretability in machine learning models. This is crucial, especially in applications where decisions impact individuals’ lives, such as healthcare or finance. XAI techniques include model-agnostic methods, rule-based approaches, and generating interpretable representations of complex models. By making machine learning models more understandable, XAI promotes trust, accountability, and ethical use of AI. It addresses the black-box” nature of some advanced models
