Explainable AI (XAI)

Explainable AI, often shortened to XAI, covers methods that help people understand why an artificial intelligence system produced a particular result. An explanation might identify influential features, show how a small input change would alter the outcome, provide an interpretable rule, or summarize a model’s broader behavior. These tools are especially important when AI affects healthcare, finance, employment, public services, or safety, where users and auditors may need to detect errors and contest decisions. Explanation quality is not the same as model accuracy, and a simple-looking account can be incomplete or misleading. Effective XAI must suit its audience, remain faithful to the system being examined, disclose uncertainty, and support practical actions such as debugging, oversight, compliance, and informed review.