Anthropic

Advanced AI could pose catastrophic or existential risks to humanity.

Public input is critical to ensuring that powerful AI serves humanity’s interests.

Our Advanced AI Framework proposes mandatory independent safety testing for frontier models, transparency requirements, and government authority to block or recall dangerous AI deployments.

Frontier developers should engage at least one qualified independent evaluator to publish a review of their evaluations and risk reports. In parallel, governments and industry should develop the ecosystem of qualified independent evaluators by settin...

Promising directions we’ve identified so far include developing the capacity to detect and respond to AI systems acting outside their developers' control, and building infrastructure for containing or shutting down such systems.

We believe it would be good for the world to have the option to slow or temporarily pause frontier AI development to enable societal structures and alignment research to keep up with the advance of the technology.

Developers should test their models against each of the four Enumerated Risk categories and publish a summary of the results.

Require Covered Developers to report Critical Safety Incidents to the designated Agency within 15 days of the Covered Developer discovering the Critical Safety Incident or facts that would lead the Covered Developer to reasonably conclude that the Cr...

Absent a strong federal law, states should retain full authority to legislate.

Champion the export of American AI. As public and commercial sectors around the world increasingly adopt AI, the Trump administration should continue its efforts to promote the global adoption of trusted AI hardware and models developed and shaped by...

Compounding the problem, labs in China often release dual-use capable models as open-weight. Once a model is open-weight, safeguards that do exist can be removed, making the model available to any state or non-state actor to use for malicious purpose...

It’s built on five core principles: keeping humans in control, aligning with human values, securing agents’ interactions, maintaining transparency, and protecting privacy.

Before approving a new model for easy or broad internal use (e.g., making the model available via our most-used internal tools), we conduct a pre-deployment review process.

We treat misalignment as a central, but solvable, problem for both us and for the field.

If it were possible to effectively slow the development of this technology to give ourselves more time to deal with its immense implications, we think that would likely be a good thing. But if a slowdown simply lets the least cautious actors catch up...

If it were possible to effectively slow the development of this technology to give ourselves more time to deal with its immense implications, we think that would likely be a good thing. But if a slowdown simply lets the least cautious actors catch up...

Importantly, the law balances [...] whistleblower protections—[...]. [...] Protecting whistleblowers: It should be an explicit violation of law [...] punish employees who raise concerns [...].

Our proposed transparency framework offers a practical first step: public visibility into safety practices while preserving private sector agility to deliver AI's transformative potential.

ASL-2 refers to systems that show early signs of dangerous capabilities – [...] give instructions on how to build bioweapons – [...] e.g. a search engine couldn’t.

we propose that AI developers pre-register large training runs [...] within their home country’s government [...] plausible bar for now would be 10^26 FLOP or higher.