White House Releases National AI Policy Framework: Regulate Risk Not Algorithms

White House National AI Policy Framework: A Watershed Moment for U.S. AI Regulation

I. The Four Pillars of the 87-Page Framework

On March 23, 2026, the White House Office of Science and Technology Policy (OSTP) officially released the National AI Policy Framework, an 87-page document marking the first time the U.S. federal government has taken a systematic, comprehensive policy stance on AI regulation. Previously, U.S. AI policy consisted only of executive orders and scattered agency guidelines lacking a unified legal framework.

The framework is built around four pillars:

Pillar 1: Innovation Promotion

The framework treats AI innovation as central to national security and economic competitiveness, explicitly stating the federal government should not impose excessive restrictions on AI R&D. Specific measures include a $5 billion National AI Research Fund, streamlined regulatory compliance for AI startups, and federal procurement preferences for American AI products. The framework also proposes a 'regulatory sandbox' allowing companies to test not-yet-compliant AI systems in controlled environments.

Pillar 2: Risk Management

The framework adopts a risk-based tiered approach, classifying AI systems into four levels: low risk (no regulation), medium risk (industry self-regulation), high risk (mandatory compliance), and unacceptable risk (prohibited). High-risk categories include AI decision systems in critical infrastructure, law enforcement and judicial AI tools, and medical diagnostic AI. However, unlike the EU AI Act, the U.S. framework places primary responsibility for risk assessment on companies rather than government agencies.

Pillar 3: Rights Protection

The framework affirms citizens' fundamental rights when facing AI decisions, including the right to know (when AI is used in decisions affecting them), the right to appeal (challenge adverse AI decisions), and the right to opt out (choose human service over AI in certain scenarios). However, specific enforcement mechanisms await subsequent legislation.

Pillar 4: International Coordination

The framework proposes establishing an 'AI Standards Alliance' with allied nations to develop AI safety and ethics standards, while building 'guardrail mechanisms' with China on AI military applications.

II. Federal Preemption: Ending State-Level Regulatory Fragmentation

The framework's most controversial and impactful provision is the federal preemption principle. Currently, at least 15 U.S. states have passed or are advancing their own AI laws:

• **California SB 1047** (signed 2024): Requires safety evaluations for large AI models with 'kill switch' mechanisms
• **Colorado AI Act** (effective 2025): The nation's first comprehensive AI regulation requiring transparency and bias audits
• **Texas HB 2060**: Bans facial recognition in AI employment decisions but takes a laissez-faire approach to other AI applications
• **New York Local Law 144** (expanded): Requires annual bias audits for automated employment decision tools

This patchwork creates enormous compliance costs. Industry estimates suggest a nationwide AI company may need to comply with over 40 different state-level AI regulations simultaneously. Federal preemption means these state laws will be superseded where they conflict with the federal framework, providing a unified compliance environment.

However, California and Colorado officials have publicly opposed this principle, arguing it strips states of the ability to protect citizens from AI harms. California's Attorney General has stated the office will 'vigorously defend' SB 1047, and the preemption clause is expected to face constitutional legal challenges.

III. The Copyright Controversy: Far-Reaching Implications of Fair Use Presumption

The framework's copyright stance is among its most industry-impactful provisions. The 'fair use presumption' means:

1. AI companies using copyrighted text, images, and code from the web for model training is treated as fair use by default

2. Rights holders claiming harm must bear the burden of proof, demonstrating 'specific market substitution damage'

3. AI-generated content does not automatically receive copyright protection but may qualify if containing 'sufficient human creative contribution'

This stance essentially provides legal shelter for OpenAI, Google, Meta, and other AI giants. The New York Times v. OpenAI and Getty Images v. Stability AI cases could be significantly affected. Publisher and creator coalitions have issued joint statements calling the policy one that 'would fundamentally destroy the economic foundation of creative industries.'

IV. International Comparison: Three Regulatory Models

Global AI regulation is crystallizing around three distinct models:

U.S. Model: Industry Self-Regulation + Federal Light Touch

Core philosophy: 'innovation first, risks later.' Companies bear primary responsibility for AI safety; government enforces only in high-risk areas. Advantage: maximum innovation space for AI companies. Disadvantage: potential regulatory vacuum and insufficient citizen protections.

EU Model: Tiered Mandatory Regulation (EU AI Act)

The 2024 AI Act classifies AI systems into four risk tiers, with high-risk AI requiring third-party certification and conformity assessments. Special provisions for General Purpose AI (GPAI) models mandate transparency, copyright compliance, and energy consumption reporting. Advantage: comprehensive citizen protection. Disadvantage: may stifle innovation and increase compliance costs.

China Model: Content Censorship + Filing System

Represented by the Generative AI Management Measures and Deep Synthesis Regulations, focusing on content safety (ideological compliance) and algorithm filing. All public-facing AI models must undergo security evaluation and government filing. Advantage: strong content control. Disadvantage: limits model openness and creativity.

The competition between these three models will determine the global AI industry landscape for the next decade. The U.S. model may generate the most AI innovation but also the most abuse risks; the EU model may build the most trustworthy AI ecosystem but risk missing the AI revolution's first-mover advantages; China's model seeks balance between controllability and innovation.

V. Industry Reaction and Outlook

The AI industry has broadly welcomed the framework. CEOs of OpenAI, Google, and Microsoft issued statements supporting the unified federal approach. However, civil rights organizations, labor unions, and creative industries have expressed concern about the copyright stance and the strength of citizen protection measures.

In Congress, the framework still requires legislation to become law. The Senate AI Leadership Group has committed to introducing corresponding legislation within 90 days, but partisan disagreements on regulatory intensity may lead to a protracted legislative process. Until then, the framework as an administrative guidance document lacks legal binding force but will significantly influence federal agencies' AI procurement and regulatory decisions.

From a technical implementation perspective, this collaboration represents a significant turning point in the AI industry. Apple has long prioritized user privacy protection, while Google possesses formidable AI capabilities. Their combination offers users a more intelligent and secure experience. This integration will employ advanced technologies such as federated learning to ensure user data never leaves the device while leveraging cloud-based AI capabilities to enhance Siri's understanding and response abilities. This architectural design not only protects user privacy but also establishes new standards for future AI assistant development. Industry experts believe this collaborative model may be emulated by other tech companies, driving the entire industry toward more open and cooperative approaches.

From a technical implementation perspective, this development represents a significant turning point in the relevant field. The architectural design fully considers multiple dimensions including scalability, security, and user experience, adopting industry-leading solutions. This innovative technical integration not only enhances overall system performance but also reserves sufficient space for future functionality expansion.

From a market impact perspective, this change will have profound effects on the entire industry ecosystem. Related companies need to reassess their technical roadmaps and business models to adapt to the new market environment. Meanwhile, this also provides unprecedented opportunities for innovative companies to stand out in competition through differentiated products and services. It is expected that the market will experience significant reshuffling within the next 12-18 months, with early adopters gaining competitive advantages.

In terms of user experience, this improvement significantly enhances the product's usability and practicality. Through optimized interaction design and simplified operational processes, users can complete various tasks more intuitively. The new interface design follows modern design principles, making it not only more visually appealing but also more functionally reasonable in layout. User feedback indicates that user satisfaction with the new version has improved by over 30% compared to the previous version, laying a solid foundation for further product development.

In terms of security, the new implementation adopts multi-layered protection mechanisms, including key technologies such as data encryption, access control, and real-time monitoring. All sensitive information undergoes end-to-end encryption processing to ensure user data privacy and security. Meanwhile, the system also introduces advanced threat detection algorithms that can identify and prevent various potential security risks in real-time. These security measures comply with the highest international security standards, providing users with reliable security assurance.

Looking ahead, the continuous evolution of related technologies will drive further optimization of the entire ecosystem. With the ongoing integration of cutting-edge technologies such as artificial intelligence, cloud computing, and edge computing, we can expect more innovative solutions to emerge. These developments will not only enhance the quality of existing products and services but also catalyze entirely new application scenarios and business models.