AI Briefing — OMB draft guidance for regulating AI applications
The White House Office of Science and Technology Policy released draft OMB guidance directing federal agencies to use risk-based, evidence-driven approaches when regulating AI applications, signaling how U.S. departments should balance innovation with protections for privacy, safety, and civil rights.
Executive briefing: On , the White House Office of Science and Technology Policy released draft OMB guidance describing how U.S. federal agencies should regulate artificial intelligence applications. The memo emphasizes risk-based analysis, public participation, and transparency obligations to safeguard privacy, civil rights, and safety without stifling innovation. It foreshadows the expectations agencies will apply to vendors seeking federal approvals or contracts involving AI.
What changed
- Ten principles—public trust, public participation, scientific integrity, risk assessment, benefits and costs, flexibility, fairness, disclosure, safety and security, and interagency coordination—set the baseline for future AI regulations and guidance.
- Agencies are directed to prioritize outcomes-based approaches and to assess proportionality before imposing prescriptive technical requirements on AI systems.
- The draft instructs regulators to promote voluntary consensus standards and to engage in pilot programs before crafting binding rules.
Why it matters
- Signals how civilian agencies will evaluate AI-enabled products and services, shaping procurement expectations and compliance roadmaps for contractors.
- Sets a national position on balancing innovation with safeguards, influencing state and international coordination on AI governance.
- Establishes that transparency, explainability, and human oversight will be recurring demands in agency approvals and audits.
Action items for operators
- Map current and planned AI uses against the memo's risk-assessment and fairness principles to prepare for future procurement language or rulemaking.
- Document model governance practices—data lineage, validation, human-in-the-loop controls, and monitoring—to evidence compliance with transparency and oversight expectations.
- Track agency-specific implementations (e.g., sector regulators or grants programs) to align disclosures and assurance artifacts ahead of solicitations.