• Federal AI Regulatory Agency (FARA)
  A centralized, independent body (potentially under the FTC or Commerce) to set standards, register high-risk systems, accredit auditors, and enforce compliance.

• Risk-Based Tiers

  • High-Risk (healthcare, finance/credit, law enforcement, defense): requires audits, explainability, monitoring, and human oversight.

  • Medium-Risk (HR screening, logistics, large-scale advertising): periodic reviews and transparency reports.

  • Low-Risk (chatbots, recommenders): light-touch rules focused on privacy and security hygiene.

• Privacy & Security
  Consent, minimization, anonymization, and secure development practices.

• Transparency & Accountability
  Explainable AI (XAI), audit trails, public registries for high-risk systems, and record-keeping requirements.

• Incentives for Ethical AI
  Tax credits, grants, and recognition labels to reward compliance, especially for small and mid-sized businesses.

• Public-Private Research Partnerships
  Grants for AI safety, bias mitigation, and standards development in collaboration with academia and industry.

• Timeline

  • 2025–2026: Stand up FARA, publish draft rules, pilot voluntary audits.

  • 2027: Mandatory registration and audits for high-risk systems.

  • 2028–2030: Full enforcement and iterative updates to align with evolving technology.

Who’s in charge (and how it works)

Federal AI Regulatory Agency (FARA)
Housed independently (or within FTC/Commerce), FARA would:

• Set binding rules

• Publish technical standards with NIST

• Register high-risk systems

• Accredit auditors

• Enforce through civil penalties, consent decrees, and corrective action plans

Advisory & coordination layer

• Interagency council (FTC, DOJ, EEOC, CFPB, HHS, DOT, DHS, DoD, Fed/FSOC for model risk) to align sector rules.

• State liaison program to preempt patchworks while honoring state privacy laws.

Risk tiers & obligations

High-Risk (e.g., healthcare, finance/credit, employment, law enforcement, safety-critical autonomy, defense):

• Pre-deployment: Impact assessment; privacy assessment; bias testing; red-team; human-in-the-loop design; model cards/datasheets; security plan.

• Runtime: Continuous monitoring for drift; incident reporting; logging; recourse mechanisms for affected individuals.

• Oversight: Annual third-party audits; re-certification upon significant model updates; explainability commensurate with decisions’ stakes.

Medium-Risk (e.g., HR screening, logistics optimization, targeted advertising at scale):

• Periodic reviews, transparency reports, basic explainability, opt-out where feasible.

Low-Risk (e.g., customer service chatbots, simple recommenders):

• Light-touch rules focused on privacy, basic disclosures, and security hygiene.

Privacy & security baseline (all tiers)

• Explicit, informed consent where applicable; purpose limitation; data minimization.

• Anonymization/pseudonymization for training/inference data where possible.

• Secure development lifecycle (threat modeling, SBOMs for AI stacks, key management).

Transparency & record-keeping

• Explainable AI (XAI) commensurate with risk and audience (consumer-facing vs. professional).

• Audit trails: training data provenance, evaluation results, known limitations, and change logs.

• Model & system cards posted to a public registry for high-risk categories.

Corporate governance requirements

• Appoint an AI Ethics/Compliance Officer (or committee) with board visibility.

• Maintain an AI risk inventory and tier classification across all business units.

• Train staff on bias, privacy, safety; set escalation pathways; protect whistleblowers.

Compliance mechanics

• Registration of high-risk deployments with FARA.

• Accredited third-party audits (annual) for high-risk; desk reviews for medium-risk.

• Incident reporting (security, safety, systemic bias); corrective action deadlines.

Incentives (so it’s not just “thou shalt not”)

• Tax credits/grants for small and medium enterprises adopting certified ethical AI controls.

• Public recognition/certification labels for compliant vendors—valuable in procurement.

Public-private research & standards

• Competitive grants for bias reduction, safety evaluation, red-teaming, transparency tooling.

• Partnerships with academia and standards bodies (NIST/ISO/IEEE) to harmonize test suites.

Timeline (aggressive but doable)

• 2025–2026: Stand up FARA; publish risk taxonomy & draft rules; pilot voluntary audits in high-risk sectors.

• 2027: Mandatory registration & audits for high-risk; medium-risk transparency reports begin.

• 2028–2029: Full enforcement for high-risk; corrective action pathways; public scorecards.

• 2029–2030: Iterative updates to rules; expand tooling grants; international alignment reviews.

What this means for businesses

SMBs

• Focus on low/medium-risk uses; qualify for credits/grants to offset compliance; templated policies and audit-lite pathways.

Large enterprises / model developers

• Higher upfront costs (audits, monitoring, documentation) but market advantage via certification and trust; clearer national rules replace state patchwork.

Consumers & workers

• Stronger privacy, clear explanations, channels for recourse, and a measurable reduction in algorithmic harm in jobs, credit, healthcare, and safety.

Implementation playbook (practical steps)

• Classify every AI system by risk tier; build a single inventory.

• Gap-assess against required controls (privacy, security, XAI, monitoring).

• Stand up governance (officer/committee, training, escalation, whistleblower).

• Prepare documentation (model/data cards, evals, audit trail).

• Select an auditor (pilot now, formalize when rules land).

• Launch continuous monitoring (bias/drift dashboards, incident playbooks).

• Apply for incentives (credits/grants) once controls are in place.

Coordination & alignment

• States: Federal preemption for core AI safety + a floor for rights; states retain complementary privacy enforcement.

• International: Map controls to EU AI Act / ISO/IEC standards to reduce cross-border friction.

Measuring success (public metrics)

• % of high-risk systems audited and compliant; median time-to-remediation.

• Reduction in substantiated bias incidents (employment, credit, healthcare).

• Adoption rates of certified ethical AI among SMBs; public trust indices.

• For SMBs: Easier compliance pathways, grants, and credits to offset costs.

• For Large Enterprises: Higher obligations but clearer national rules and reputational benefits through certification.

• For Consumers & Workers: Stronger privacy, recourse options, and reduced algorithmic harm in jobs, healthcare, credit, and safety.

This policy framework was driven from ChatGPT research.
View the original research here.