Principle of explicability and accountability in artificial intelligence systems. Covers model cards, dataset documentation, disclosure of architectures and training data, traceability of algorithmic decisions, interpretability of black-box models, EU AI Act regulatory requirements for high-risk systems, audit trails of inferences, management of bias and fairness metrics, documentation of limitations and failure modes, and the right to explanation for automated decisions.