Public-Sector AI Audit Registers and Transparency Practices

Abstract

Background: Recent developments involving AI audits have made implementation quality, oversight, and administrative capacity central policy concerns.

Objective: The analysis examined how policy relating to AI audits operated in practice and whether differences in capacity and oversight were associated with outcomes in relation to transparency.

Methods: A structured review covered 882 statutes, regulations, policies, contracts, guidance documents, administrative records, or public disclosures. A predefined two-stage coding framework assessed legal clarity, transparency, procedural safeguards, oversight, and implementation requirements, with discrepancies resolved through independent review.

Results: Although 59% of reviewed materials contained formal provisions on AI audits, only 39% translated those provisions into sufficiently detailed implementation or oversight requirements. Transparency and review were deficient in 11% of cases, while more comprehensive instruments tended to assign responsibility more clearly and leave fewer gaps.

Conclusion: The results shift attention from whether rules on AI audits exist to how those rules are administered in practice. This means that AI governance in the public sector needs enforceable controls, capable staff, documented accountability, and meaningful human review.

Study design: registry audit study.

Publication history: Originally published in print on 15 June 2026, Volume 6, Article 3. Online abstract record added on 16 September 2026. Full-text PDF will be added when the digital galley is ready.