COMPEL Glossary / ai-incident-classification
AI Incident Classification
AI Incident Classification is a systematic framework for categorizing AI failures, malfunctions, and harmful outputs by their severity, impact scope, root cause type, and urgency of required response.
What this means in practice
Classification schemes typically define severity tiers ranging from minor degradation to critical safety events, with each tier triggering different response protocols, escalation paths, and communication requirements. For organizations operating AI in production, a well-designed classification system ensures that critical incidents receive immediate attention while routine issues follow standard resolution processes without overwhelming response teams. In COMPEL, incident classification is part of the operational resilience framework discussed in Module 2.4, Article 12, and connects to the broader AI risk governance architecture at the enterprise level in Module 3.4, Article 5.
Why it matters
Without a systematic framework for categorizing AI incidents by severity and urgency, organizations either over-respond to minor issues (wasting resources) or under-respond to critical failures (allowing harm to escalate). A well-designed classification system ensures proportionate response and prevents the organizational paralysis that occurs when teams lack clear protocols for distinguishing between routine degradation and safety-critical events.
How COMPEL uses it
Incident classification is designed during the Model stage as part of the operational resilience framework within the Governance pillar. Severity tiers, response protocols, and escalation paths are formalized during Produce. The Evaluate stage analyzes incident patterns using the classification system to identify systemic risks and improvement opportunities. The Learn stage uses classified incident data to refine response procedures and prevent recurrence across subsequent COMPEL cycles.
Related Terms
Other glossary terms mentioned in this entry's definition and context.