A review process that evaluates whether AI explainability outputs meet internal policies and regulatory requirements, ensuring sufficient transparency.
A systematic examination - by internal compliance teams or external auditors - of explanation artifacts, comparing them against policy checklists (e.g., “Does every high-risk decision include a counterfactual explanation?”), regulatory mandates (e.g., GDPR right to explanation), and stakeholder needs. The audit assesses both technical correctness and usability, documents findings, and issues remediation plans for any gaps in explanation coverage or quality.
A financial regulator conducts an XAI Audit on a bank’s credit-scoring AI. Auditors sample denials, review corresponding explanations, verify that each includes clear rationales and remedial suggestions, and confirm that explanation logs are retained for the required five-year period. Any missing or inadequate explanations prompt corrective action by the bank.
