Sharing Compliance Framework for California’s Transparency in Frontier AI Act
On January 1, California’s Transparency in Frontier AI Act (SB 53) will come into effect, establishing the nation’s first safety and transparency requirements for catastrophic risks associated with frontier AI development.
While advocating for a federal framework, the endorsement of SB 53 underscores the belief that frontier AI developers must be transparent about their risk assessment and management practices. This law aims to balance robust safety practices, incident reporting, and whistleblower protections while allowing flexibility in implementation and exempting smaller companies from unnecessary regulatory burdens.
Key Components of the Frontier Compliance Framework
One of the primary requirements of SB 53 is that frontier AI developers publish a framework detailing how they assess and manage catastrophic risks. The Frontier Compliance Framework (FCF) is now publicly available, outlining:
- Assessment and Mitigation: The FCF details the approach to assessing and mitigating risks from cyber offense, chemical, biological, radiological, and nuclear threats, along with risks of AI sabotage and loss of control.
- Tiered System: A tiered system for evaluating model capabilities against these risk categories is established.
- Safety Incident Response: The framework explains the approach to protecting model weights and responding to safety incidents.
Much of the FCF reflects practices already adopted since 2023 under the Responsible Scaling Policy (RSP), which has guided decisions regarding managing extreme risks from advanced AI systems. The release of detailed system cards for new models has also been part of this approach, ensuring transparency in capabilities, safety evaluations, and risk assessments. With the new law, such transparency practices become mandatory for the most powerful AI systems in California.
The Need for a Federal Standard
The implementation of SB 53 marks a significant moment in AI regulation. By formalizing transparency practices that responsible labs voluntarily follow, the law ensures these commitments remain intact as AI models become more capable and competition intensifies. However, a federal AI transparency framework is still needed to ensure consistency across the country.
Earlier proposals for federal legislation emphasize public visibility into safety practices without locking in specific technical approaches. Key tenets of this proposed framework include:
- Public Secure Development Framework: Developers should publish frameworks detailing risk assessment and mitigation strategies.
- System Cards at Deployment: Documentation summarizing testing, evaluation, and results must be publicly disclosed upon model deployment.
- Whistleblower Protection: It should be illegal for labs to misrepresent compliance or retaliate against employees raising concerns.
- Flexible Transparency Standards: A minimum set of standards that enhances security and public safety while adapting to the evolving nature of AI development.
- Limiting Application: Requirements should focus on large developers to avoid burdening smaller startups.
As AI systems continue to grow in power and capability, public visibility into their development and safety measures is essential. The collaboration with Congress and the administration aims to create a national transparency framework that ensures safety while preserving America’s leadership in AI technology.
