Explainable AI (XAI)

Explainable AI helps reveal how and why an AI or machine learning model produces a particular output. XAI techniques can identify influential features, visualize decision processes, or provide human-readable explanations for predictions. Methods such as LIME, SHAP, feature importance, and attention visualization can help interpret models that would otherwise operate as black boxes.

Understanding AI decisions is important when models influence high-impact or regulated processes. XAI can help teams identify bias, investigate incorrect predictions, improve model transparency, and support accountability. It also enables users, developers, and auditors to better evaluate whether an AI system is behaving as intended.