COMPEL Glossary / algorithmic-audit
Algorithmic Audit
An algorithmic audit is an independent, systematic examination of an AI system's decision-making processes, data inputs, outputs, and real-world impacts to assess whether the system operates in compliance with legal requirements, ethical standards, and organizational policies.
What this means in practice
Audits may be conducted by internal audit teams, external auditors, or specialized third-party firms, and can be triggered by regulatory requirements, stakeholder concerns, or routine governance practices. For organizations deploying AI in high-stakes domains, algorithmic audits provide external validation that internal governance controls are working as intended. In COMPEL, algorithmic audits are part of the assurance framework covered in Module 3.4, Article 8, and are connected to the Evaluate stage where audit findings inform the next cycle of improvement.
Why it matters
Internal governance controls, no matter how well-designed, require independent verification to ensure they are working as intended. Algorithmic audits provide external validation that gives regulators, customers, and stakeholders justified confidence in AI governance practices. Organizations that build audit readiness into their governance operations avoid the costly scramble of assembling evidence when audits are announced, maintaining continuous compliance readiness.
How COMPEL uses it
Algorithmic audits are part of the assurance framework within the Governance pillar, connected to the Evaluate stage where audit findings inform the next improvement cycle. During Model, audit requirements and readiness criteria are designed. The Produce stage establishes the evidence generation practices that make audits efficient, and the Learn stage incorporates audit findings into methodology refinements and governance improvements for subsequent COMPEL cycles.
Related Terms
Other glossary terms mentioned in this entry's definition and context.