ModelRefs / AI Audit — AI Glossary
AI Audit — AI Glossary
A systematic review of an AI system's design, training data, outputs, and deployment practices to assess compliance, fairness, and risk.
Overview
AI audits examine: training data provenance, model performance across demographic groups, decision process transparency, security vulnerabilities, and policy compliance. Methodologies: third-party technical audits, algorithmic impact assessments (AIA), red-teaming. Mandated for high-risk AI under the EU AI Act; voluntary for most US deployments.
Reference details
| Topic | safety |
|---|---|
| Last reviewed | 2026-06-24 |
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Frequently asked questions
What is AI Audit?
A systematic review of an AI system's design, training data, outputs, and deployment practices to assess compliance, fairness, and risk.
What concepts are related to AI Audit?
Closely related concepts include nist ai rmf, responsible ai, model risk management.