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COMPEL Body of Knowledge

COMPEL Glossary / GL-26

Explainability Requirements

A documented specification of the minimum explainability standard each AI system must meet — defining what must be explainable, to whom, in what form, and within what timeframe — aligned to the system's risk classification, regulatory context, and stakeholder needs.

What this means in practice

It ensures that explainability is treated as a governance requirement rather than a desirable feature.

Context in the COMPEL framework

Produced in the Model stage alongside the Control Requirements Matrix. Explainability requirements directly inform the technical design choices made during the Produce stage.

Where you see this

Explainability Requirements is most commonly referenced when teams work across the Model and Produce stages — especially within the Operational Readiness layer . It appears in governance artifacts, assessment instruments, and delivery playbooks wherever COMPEL is operationalized.

Related COMPEL stages

Related domains

Synonyms

XAI requirements , interpretability requirements , transparency requirements

Related Terms Network

This diagram shows how Explainability Requirements connects to other concepts in the COMPEL body of knowledge. Each linked node is a term you can explore in turn.

Related terms for Explainability Requirements Network graph showing 3 related glossary terms connected to Explainability Requirements. Explainability… AI System … Control Re… Human Vali…
3 related concepts connected to Explainability Requirements.
Accessible list of related terms
  • AI System Classification Register — A formal register that classifies every AI system in scope according to risk tier, autonomy level, data sensitivity, regulatory applicability, and criticality — producing a system-level risk profile that determines which governance controls, review processes, and compliance requirements apply.
  • Control Requirements Matrix — A comprehensive mapping of every governance control required for each AI system — specifying the control type (preventive, detective, corrective), the risk or policy it addresses, the evidence required to prove effectiveness, the owner, and the testing frequency.
  • Human Validation Rules — A formal specification of every decision point in an AI system's operation where human review, approval, or override is required — including the trigger conditions, review timeline, escalation path, and documentation requirements for each rule.

See also

  • AI System Classification Register — A formal register that classifies every AI system in scope according to risk tier, autonomy level, data sensitivity, regulatory applicability, and criticality — producing a system-level risk profile that determines which governance controls, review processes, and compliance requirements apply.
  • Control Requirements Matrix — A comprehensive mapping of every governance control required for each AI system — specifying the control type (preventive, detective, corrective), the risk or policy it addresses, the evidence required to prove effectiveness, the owner, and the testing frequency.
  • Human Validation Rules — A formal specification of every decision point in an AI system's operation where human review, approval, or override is required — including the trigger conditions, review timeline, escalation path, and documentation requirements for each rule.

Related Terms

Other glossary terms mentioned in this entry's definition and context.