Define explainability

Define explainability refers to the capacity of artificial intelligence systems to provide clear, understandable explanations for their decisions, predictions, and internal processes in human-comprehensible terms. This critical AI property encompasses interpretability methods that reveal how models process inputs and generate outputs, enabling stakeholders to understand, trust, and validate AI system behavior. Explainability techniques include feature importance analysis, attention visualization, LIME (Local Interpretable Model-agnostic Explanations), and SHAP (SHapley Additive exPlanations) values that highlight influential factors in decision-making. The concept spans global explainability revealing overall model behavior and local explainability explaining individual predictions. Regulatory frameworks increasingly mandate explainable AI in high-stakes domains like healthcare, finance, and criminal justice. For AI agents, explainability ensures transparent decision-making, enables debugging and improvement, builds user trust, and supports regulatory compliance essential for responsible deployment.