Explainable AI refers to a set of techniques and methods in artificial intelligence that make the behavior and decision-making processes of AI models understandable and transparent to humans. Unlike traditional “black-box” AI models, which operate without providing insight into how they arrive at specific conclusions, explainable AI ensures that the reasoning behind AI-driven decisions can be traced and understood.
The goal of XAI is to provide clear, interpretable explanations of an AI system’s actions, allowing users to trust its outputs. This is particularly important in industries like healthcare, finance, and law, where decisions made by AI can have significant consequences. Explainability improves transparency, accountability, and reduces the risk of unintended biases, helping users and stakeholders to understand and validate AI-driven results.