Tier 2: Evidence-grade controls

Explainability and stakeholder rationale

The ability to provide a human-understandable explanation for how an AI system influences outcomes, tailored to regulators, customers, and affected individuals.

Board-defensible evidence

• Explainability standard defining what must be explainable for the use case (inputs, decision factors, confidence, limitations), who the explanation is for, and the minimum acceptable explanation format.
• Evidence of explanation artifacts such as model cards, decision rationale templates, feature importance summaries, and user-facing disclosures, each versioned and approved for use.
• Testing records showing that explanations are accurate, consistent, and not misleading, including review by legal and compliance for consumer or stakeholder communications.
• Operational procedures showing how explanations are delivered on request (customer support workflow, regulator response workflow), including response SLAs and required approvers.
• Exception documentation for models that cannot be fully explained, including risk justification, compensating controls, and approval by risk and legal with periodic re-evaluation dates.

Why this matters

If you cannot explain outcomes credibly, the narrative will be written by regulators, plaintiffs, or the press, not your governance team.