Explainability meaning

Explainability meaning refers to the fundamental concept of making artificial intelligence systems’ decision-making processes, reasoning patterns, and internal mechanisms comprehensible and interpretable to humans in actionable terms. This core AI principle encompasses the ability to provide clear, logical explanations for model predictions, feature importance, and algorithmic behavior that stakeholders can understand and validate. Explainability meaning extends beyond simple output generation to include transparency in model architecture, training processes, and decision pathways. The concept encompasses both global explainability that reveals overall system behavior and local explainability that explains individual predictions. This fundamental requirement enables trust building, regulatory compliance, bias detection, and system debugging. For AI agents, explainability meaning ensures transparent autonomous decision-making, supports accountability frameworks, and enables human oversight essential for responsible AI deployment.