Explainable Ai And Interpretable Machine Learning: Illuminating The Black Box

Explainable AI (XAI) and Interpretable Machine Learning are two groundbreaking approaches that aim to make the decisions of AI models transparent and comprehensible. XAI techniques range from generating textual explanations to highlighting relevant features and data points that influenced a decision, while Interpretable Machine Learning focuses on designing models that are inherently understandable. These concepts are particularly relevant in domains where decision justifications are crucial, such as healthcare, finance, and law enforcement.