A uniform federal approach would reduce compliance complexity for AI vendors that otherwise must navigate divergent state laws and standards. This lowers transaction costs for multi‑state deployments and speeds product rollouts, which benefits end users through faster access to improved tools. Eliminating the patchwork of state rules that already forces vendors to bake differing legal requirements into models would be a darn good thing.
A national policy can also concentrate expertise and enforcement at federal agencies, enabling consistent guidance on safety, transparency, and liability. Centralized standards make it easier for purchasers and regulators to compare offerings and for providers to certify compliance across jurisdictions. The current Administration’s AI Action Plan frames this as part of a broader industrial strategy to maintain U.S. competitiveness.
The following is a breakdown of why I support a national AI standard.
Key Benefits For Providers
- Regulatory predictability: One set of rules reduces legal uncertainty and compliance costs.
- Faster innovation cycles: Less time spent tailoring models to state regimes means more resources for R&D.
- Scale economics: Uniform rules enable national rollouts and consistent contracting practices.
Key Benefits For Users
- Broader access to AI tools as providers scale faster.
- Easier comparability of vendor claims when federal standards define key metrics (e.g., bias testing, robustness).
Tradeoffs And Risks
- Civil‑rights and consumer‑protection concerns: State laws often reflect local priorities (e.g., anti‑discrimination protections). Preemption risks weakening protections unless federal rules explicitly safeguard them.
- Enforcement concentration: Relying on federal agencies may create enforcement gaps or politicization; multiagency approaches can be powerful but complex.
- One‑size‑fits‑all limits: National standards may not fit niche sectors (healthcare, finance) without tailored subrules.
Practical Guidance For Providers And Users Today
- Providers should document compliance programs, invest in robust bias and safety testing, and engage with federal rulemaking to shape workable standards. Maintain modular design so models can be adapted if state rules persist. 2
- Users (buyers and procurement officers) should demand contractual warranties on testing, transparency about training data and limitations, and clear remediation paths for harms. Insist on audit rights and independent validation. 1
Conclusion And Recommendations
A national AI framework can unlock innovation and simplify compliance but only if it pairs clear safety and enforced guardrails with meaningful enforcement—accountability is key for any regulatory scheme. Policymakers should balance preemption with sectoral nuance; providers must prepare for both federal standards and potential state pushback; users should insist on contractual protections. Staying proactive—through documentation, testing, and engagement—will be the best defense and opportunity for all stakeholders.
David Seidman is the principal and founder of Seidman Law Group, LLC. He serves as outside general counsel for companies, which requires him to consider a diverse range of corporate, dispute resolution and avoidance, contract drafting and negotiation, and other issues. In particular, he has a significant amount of experience in hospitality law by representing third party management companies, owners, and developers.
He can be reached at david@seidmanlawgroup.com or 312-399-7390.
This blog post is not legal advice. Please consult an experienced attorney to assist with your legal issues.
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