On March 20, 2026, the White House unveiled a comprehensive national legislative framework that aligns with its previous initiatives aimed at regulating artificial intelligence (AI). This Framework follows the AI Preemption Executive Order issued in December 2025 and the AI Action Plan released in July 2025. It addresses critical topics including child safety, privacy, AI training and copyright issues, liability protections, and the preemption of state laws. The administration is urging Congress to transform this policy blueprint into law by the end of the year, with some advisers expressing cautious optimism about the potential for a bipartisan agreement.
In conjunction with the Framework’s announcement, Senator Marsha Blackburn introduced a 291-page discussion draft for national AI legislation, proposing what she described as “one federal rulebook for AI.” While both the White House Framework and Blackburn’s draft share similar goals, such as establishing a cohesive federal approach to AI regulation, they diverge significantly on key issues like liability and preemption.
As legislators grapple with these complex issues, developers and AI deployers find themselves in a state of uncertainty. A patchwork of state laws currently governs various aspects of AI, including child protection, health and safety, and transparency measures. This fragmented landscape complicates compliance for businesses operating in multiple jurisdictions.
Key Issues in the Framework
The Framework proposes significant preemption of state AI laws deemed to impose “undue burdens” on developers and general-purpose systems. It seeks to shield developers from liability for third-party misuse of their AI models while emphasizing that states retain the authority to enforce laws designed to protect children and consumers. Unlike existing protections under Section 230 of the Communications Decency Act, which shields platforms from liability for third-party content, the proposed liability shield centers specifically on state penalties related to unlawful AI use.
In terms of child protection, the Framework advocates for tools that allow parents to manage their children’s privacy settings and features designed to mitigate risks of self-harm among minors. It also emphasizes that Congress should affirm existing child privacy protections apply to AI systems, including restrictions on data collection for training purposes.
Regarding copyright issues, the Framework adopts a cautious, pro-training stance. It asserts that using copyrighted material for training AI models does not violate copyright laws and suggests that courts, rather than Congress, should resolve related fair use questions. Notably, it calls for Congress to explore licensing regimes that would facilitate negotiations between rights holders and AI providers.
The Framework also proposes federal standards to protect individuals from unauthorized commercial use of AI-generated replicas of their likeness or voice, while recognizing exceptions for parody and other expressive works. To promote AI development, the administration encourages the establishment of regulatory sandboxes and improved access to federal datasets in AI-ready formats. Additionally, it calls for resources such as grants or tax incentives for small businesses to encourage wider AI adoption.
While the Framework and Blackburn’s draft are aligned in many respects, Blackburn’s proposal includes amendments to the Copyright Act that would explicitly state unauthorized copying for AI training does not constitute fair use. As the formal introduction of the bill awaits further negotiation, the precise language remains to be seen.
The ambitious preemption measures outlined in the Framework may face challenges in Congress. Evidence of bipartisan skepticism is illustrated by a Senate vote to strip a ten-year moratorium on state AI regulation from a budget reconciliation bill. More narrowly defined measures, particularly those focusing on child protection and digital replicas, appear to garner clearer bipartisan support.
In tandem with the White House’s efforts, the Federal Trade Commission (FTC) has adopted a light-touch approach to AI regulation, emphasizing enforcement of existing laws while addressing AI-related fraud and deceptive practices. FTC Commissioner Melissa Holyoak has stated that the agency aims to promote AI growth without imposing excessive regulation. Recent enforcement actions against companies misrepresenting their AI capabilities demonstrate this approach in action.
For AI developers and businesses, the Framework signals neither an imminent reset nor a status quo. While it is not yet law, it reflects the administration’s overarching strategy for federal AI policy. Even in the absence of enacted legislation, the Framework may deter states from implementing broad AI laws, encouraging them to await federal guidance. As state governments pursue their own regulations, like California’s executive order enhancing AI procurement standards, businesses must navigate compliance while monitoring potential federal developments.
In the evolving landscape of AI regulation, developers should focus on aligning their practices with state laws that address pressing issues such as child safety and transparency. Keeping abreast of federal legislative efforts will be critical as Congress seeks to establish a national standard for AI practices.
See also
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