Instructs federal agencies to prioritize AI innovation, governance, and public trust while removing bureaucratic barriers. Requires development of an AI strategy and appointment of a Chief AI Officer. Mandates risk management practices for high-impact AI applications and ensures transparency and accountability.
Good coverage — Comprehensive governance measures or mitigations with clear procedures relating to the risk subdomain are explicitly described.
Minimal coverage — Brief mention but no specific mitigations, recommendations or governance measures relating to the risk subdomain are described.
All other risk subdomains had no coverage in this document.
Risk Coverage Summary
The document has good coverage of approximately 10-12 subdomains, with strong focus on AI system security (2.2), governance failure (6.5), lack of robustness (7.3), lack of transparency (7.4), competitive dynamics (6.4), and various discrimination and safety risks. Coverage is concentrated in system safety, governance, and operational risk domains.
Good coverage — Comprehensive governance measures or mitigations with clear procedures relating to the sector are explicitly described.
Minimal coverage — Brief mention but no specific mitigations, recommendations or governance measures relating to the sector are described.
All other sectors had no coverage in this document.
Sectors Summary
This document governs AI use across all federal government operations, which spans multiple sectors including Public Administration (primary focus), National Security, Healthcare, Law Enforcement, Education, Transportation, and other government services. The governance applies to federal agencies as they develop, procure, and deploy AI systems for public services and government operations.
Hard Law
Legislative Status Summary
This is a binding executive memorandum from the Office of Management and Budget with mandatory requirements for federal agencies, enforcement mechanisms including compliance reporting, and consequences for non-compliance including discontinuation of AI use.
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Lifecycle stages covered by the documents, as defined in the OECD Framework for the Classification of AI Systems
AI Lifecycle Summary
The document comprehensively covers all AI lifecycle stages with particular emphasis on deployment, operation and monitoring. It addresses planning through AI strategy development, data governance, model development through procurement and custom development, verification through pre-deployment testing and impact assessments, deployment through risk acceptance processes, and ongoing monitoring through continuous performance evaluation.
Terms relating to technical scope that are mentioned or implicitly referenced in the document
Technical Scope Summary
The document explicitly mentions AI models and AI systems with detailed definitions. It does not specifically mention frontier AI, general purpose AI, task-specific AI, foundation models, generative AI (except in one policy requirement), predictive AI, or compute thresholds. It does address open source software and model weights in the context of code sharing requirements.
Document data from AGORA, CSET Emerging Technology Observatory.