Explainable AI (XAI)

Explainable AI (XAI) refers to a set of methods and techniques that aim to make artificial intelligence systems, particularly complex deep learning models, more understandable, transparent, and interpretable to humans. In contrast to "black box" AI models that produce accurate predictions but offer little insight into their decision-making process, XAI seeks to provide clear, actionable explanations for why an AI system arrived at a particular output. This includes identifying the input features that most influenced a decision, visualizing internal model processes, or generating human-readable justifications.