AI Briefing — OMB draft guidance for regulating AI applications

Executive briefing: The OMB draft memorandum on federal agency regulation of AI applications (released 7 January 2020) outlines principles for risk-based AI governance, urging agencies to avoid overregulation while ensuring transparency, safety, and fairness. This briefing converts the draft into a ready-to-use checklist with tables, timelines, and internal navigation for policy, technical, and procurement teams.

Why it matters: Agencies and vendors supporting federal AI projects need to align development, evaluation, and acquisition processes to the draft’s principles—such as public participation, interagency coordination, and performance-based regulation—while preparing for future OMB and NIST implementation guidance.

Draft principles and operationalization

90-day readiness plan

1. Days 0–30: Stand up AI inventory; classify use cases by impact; map to NIST AI RMF; publish initial transparency notices; schedule public engagement if applicable.
2. Days 31–60: Complete risk assessments and bias tests; define human oversight protocols; integrate supply-chain checks (model provenance, dataset licensing, SBOMs).
3. Days 61–90: Conduct independent review of high-impact systems; finalize performance metrics; publish documentation; prepare reporting package for OMB and agency leadership.

Lifecycle diagram

        Inventory → Risk tiering → Design controls → Testing (bias/robustness) →
        Independent review → Deployment with notices → Monitoring → Periodic recertification
            

Acquisition and vendor management

• Pre-award: Require vendors to provide model cards, data lineage, licensing, and SBOMs; include performance-based metrics in solicitations.
• Evaluation: Score proposals on transparency, bias testing plans, reproducibility, and security controls; require access for independent evaluation when risk tier is high.
• Post-award: Embed reporting obligations for incidents, drift, and retraining; require recertification on material model changes.

Oversight and documentation

• Transparency artifacts: System cards, user notices, public FAQs, and data source disclosures.
• Risk files: Bias test reports, robustness/adversarial test summaries, and mitigation plans.
• Governance: Meeting minutes of AI review boards; independent evaluator conclusions; risk acceptance memos.
• Monitoring: Drift dashboards, accuracy KPIs, human override usage, and incident log with remediation timelines.

Metrics

• Coverage: 100% of AI systems cataloged; 100% with assigned risk tier and documented owner.
• Transparency: Notices published for 100% of public-facing AI uses; response time <10 business days for public inquiries.
• Evaluation: Bias/robustness testing completed for all high and medium tiers; ≥90% of identified issues mitigated before deployment.
• Governance cadence: Quarterly review of high-impact systems; annual recertification of documentation and controls.
• Vendor compliance: 100% of new awards include AI transparency and risk clauses; ≥95% of vendors deliver SBOMs and model cards.

Stakeholder responsibilities

• Program owners: Maintain inventory entries; define mission outcomes and acceptance thresholds.
• Data scientists/engineers: Produce model cards, bias/robustness test reports, reproducibility artifacts, and drift monitors.
• Privacy/civil rights offices: Evaluate fairness and civil rights impacts; ensure notice language clarity; review data minimization.
• Security teams: Perform threat models, adversarial testing, and supply-chain validation; track SBOM updates.
• Legal/policy: Align with OMB/NIST guidance; manage public engagement and paperwork reduction considerations; document risk acceptance.
• Independent evaluators: Provide third-party review for high-impact systems before launch and annually thereafter.

Evidence and retention

Keep inventories, risk assessments, bias/robustness results, model and system cards, public-comment responses, supply-chain attestations, independent review reports, and monitoring dashboards. Retain for audit and to accelerate compliance with subsequent OMB final guidance and agency oversight.

Data and privacy alignment

• Data minimization: Document necessity for each feature; avoid unnecessary PII/PHI; apply de-identification where feasible.
• Access governance: Role-based access and least privilege for training and inference pipelines; monitor privileged activity.
• Retention and deletion: Define retention aligned to mission and legal requirements; automate deletion for expired datasets and model versions.
• FOIA/readiness: Maintain redaction-ready versions of documentation for public requests without exposing sensitive code or data.

Reporting package for leadership

1. Inventory snapshot with risk tiers and owners.
2. Metrics dashboard (accuracy, bias findings, robustness, incidents).
3. Transparency artifacts (notices, FAQs, model cards).
4. Supply-chain attestations (SBOMs, dataset provenance, licensing).
5. Independent review outcomes and remediation plans.

Diagram: documentation flow

        Data sources → Data cards → Model training → Model cards → System cards → Notices → Monitoring reports
            

Risk acceptance guardrails

Require signed risk acceptance for deployments that proceed with known issues, including description of mitigations, time-bound expiration, and triggers for rollback. Revisit at each quarterly governance review.

Use-case examples and tiering

Testing toolkit

• Bias: Stratified performance analysis by protected class proxies; counterfactual evaluation.
• Robustness: Adversarial perturbation tests; stress tests for distribution shift; fallback behaviors.
• Explainability: Provide model and system-level explanations appropriate to users; document limitations.

Ongoing monitoring

Establish alerts for drift, model confidence anomalies, and spikes in human override usage. Tie alerts to incident-response pathways with clear SLAs for investigation and rollback.

Coordination with privacy and civil rights offices

Ensure Privacy Impact Assessments and Civil Rights reviews align with the AI risk tier. Where automated decisions affect individuals, document appeal processes, human review checkpoints, and communication scripts for adverse actions.

Budget and resourcing

Estimate staffing for model governance (data scientist, privacy counsel, security engineer, product owner), independent evaluation costs, and tool spend (bias testing, monitoring, SBOM generation). Tie budget requests to the 90-day milestones to secure funding early.

Change management and audit trail

Document version history of models, datasets, and configuration changes with timestamps and approvers. Maintain immutable logs for audit, linking to risk assessments and approvals to satisfy OMB expectations for traceability.

Schedule quarterly training refreshers for staff on the draft principles, with scenario-based exercises that test documentation completeness and oversight responses.

Create a repository of exemplar notices and documentation templates so new AI projects can onboard quickly without reinventing governance artifacts, reducing time-to-compliance.

Publish a quarterly summary of AI system statuses and risk decisions to leadership, reinforcing accountability and enabling early intervention where milestones slip.