Explainable AI (XAI) encompasses methods and techniques aimed at making the decision-making processes of complex AI models understandable.
Many modern AI models, especially deep neural networks, are difficult to comprehend, which limits their acceptance in critical areas.
XAI uses various approaches, such as visual explanations, feature analysis, or rule-based systems, to provide insights into the functioning of the models.
This allows users and developers to understand which factors led to a particular decision.
Explainable AI is especially important in areas such as medicine, law, or finance, where transparency and traceability are essential.