accountability

Explores how organizations investigate AI incidents—from model failures and bias to security breaches. Outlines detection, evidence collection, and root-cause analysis across data, model, and deployment layers. Emphasizes documentation, stakeholder communication, and corrective actions, embedding lessons into governance to reduce risk and improve model reliability.