GAO Issues AI Accountability Framework — June 30, 2021
The U.S. Government Accountability Office released an AI accountability framework guiding federal agencies on governance, data quality, performance, and monitoring controls.
GAO-21-519SP translates audit practices into an AI lifecycle framework covering four principles—Governance, Data, Performance, and Monitoring—and associated auditing questions. It emphasises documenting responsibilities, ensuring data integrity, testing models, and tracking outcomes for equity and mission effectiveness.
- Governance. Agencies must define roles, risk tolerance, and oversight mechanisms.
- Data. Teams should document provenance, representativeness, and privacy safeguards.
- Performance and monitoring. The framework promotes pre-deployment testing, continuous evaluation, and incident response planning.
The GAO guidance aligns closely with NIST’s AI RMF and supports Zeph Tech’s federal assurance engagements.
Continue in the AI pillar
Return to the hub for curated research and deep-dive guides.
Latest guides
-
AI Workforce Enablement and Safeguards Guide — Zeph Tech
Equip employees for AI adoption with skills pathways, worker protections, and transparency controls aligned to U.S. Department of Labor principles, ISO/IEC 42001, and EU AI Act…
-
AI Incident Response and Resilience Guide — Zeph Tech
Coordinate AI-specific detection, escalation, and regulatory reporting that satisfy EU AI Act serious incident rules, OMB M-24-10 Section 7, and CIRCIA preparation.
-
AI Model Evaluation Operations Guide — Zeph Tech
Build traceable AI evaluation programmes that satisfy EU AI Act Annex VIII controls, OMB M-24-10 Appendix C evidence, and AISIC benchmarking requirements.