"The global nature of AI risks makes it necessary to recognize AI safety as a global public good"

· Consensus Statement on AI safety as a Global Public Good

The global nature of these risks from AI makes it necessary to recognize AI safety as a global public good, and work towards global governance of these risks.

Promising initial steps by the international community show cooperation on AI safety and governance is achievable despite geopolitical tensions.

Thanks to these summits, states established AI safety Institutes or similar institutions to advance testing, research and standards setting.

States must sufficiently resource AI safety Institutes, continue to convene summits and support other global governance efforts.

Deep and foundational research needs to be conducted to guarantee the safety of advanced AI systems.

To enable this, we call on states to carve out AI safety as a cooperative area of academic and technical activity, distinct from broader geostrategic competition on development of AI capabilities.

through which domestic AI safety authorities convene, collaborate on, and commit to implement model registration and disclosures, incident reporting, tripwires, and contingency plans.

· A safety Assurance Framework,

requiring developers to make a high confidence safety case prior to deploying models whose capabilities exceed specified thresholds.

These safety assurances should be subject to independent audits.

· Independent Global AI safety and Verification Research,

developing techniques that would allow states to rigorously verify that AI safety related claims made by developers, and potentially other states, are true and valid.

To facilitate these agreements, we need an international body to bring together AI safety authorities, fostering dialogue and collaboration in the development and auditing of AI safety regulations across different jurisdictions.

To facilitate these agreements, we need an international body to bring together AI safety authorities, fostering dialogue and collaboration in the development and auditing of AI safety regulations across different jurisdictions.

This body would ensure states adopt and implement a minimal set of effective safety preparedness measures, including model registration, disclosure, and tripwires.

Over time, this body could also set standards for and commit to using verification methods to enforce domestic implementations of the safety Assurance Framework.

Experts and safety authorities should establish incident reporting and contingency plans, and regularly update the list of verified practices to reflect current scientific understanding.

· safety Assurance Framework

This is insufficient to provide safety guarantees for advanced AI systems.

Developers should submit a high confidence safety case, i.e., a quantitative analysis that would convince the scientific community that their system design is safe, as is common practice in other safety critical engineering disciplines.

Developers should submit a high confidence safety case, i.e., a quantitative analysis that would convince the scientific community that their system design is safe, as is common practice in other safety critical engineering disciplines.

Developers should submit a high confidence safety case, i.e., a quantitative analysis that would convince the scientific community that their system design is safe, as is common practice in other safety critical engineering disciplines.

Additionally, safety cases for sufficiently advanced systems should discuss organizational processes, including incentives and accountability structures, to favor safety.

Additionally, safety cases for sufficiently advanced systems should discuss organizational processes, including incentives and accountability structures, to favor safety.

Further assurance should be provided by automated run time checks, such as by verifying that the assumptions of a safety case continue to hold and safely shutting down a model if operated in an out of scope environment.

Further assurance should be provided by automated run time checks, such as by verifying that the assumptions of a safety case continue to hold and safely shutting down a model if operated in an out of scope environment.

States have a key role to play in ensuring safety assurance happens.

Additionally, for models exceeding early warning thresholds, states could require that independent experts approve a developer’s safety case prior to further training or deployment.

While there may be variations in safety Assurance Frameworks required nationally, states should collaborate to achieve mutual recognition and commensurability of frameworks.

· Independent Global AI safety and Verification Research

Independent research into AI safety and verification is critical to develop techniques to ensure the safety of advanced AI systems.

Independent research into AI safety and verification is critical to develop techniques to ensure the safety of advanced AI systems.

States, philanthropists, corporations and experts should enable global independent AI safety and verification research through a series of Global AI safety and Verification Funds.

States, philanthropists, corporations and experts should enable global independent AI safety and verification research through a series of Global AI safety and Verification Funds.

In addition to foundational AI safety research, these funds would focus on developing privacy preserving and secure verification methods, which act as enablers for domestic governance and international cooperation.

These methods would allow states to credibly check an AI developer’s evaluation results, and whether mitigations specified in their safety case are in place.

In the future, these methods may also allow states to verify safety related claims made by other states, including compliance with the safety Assurance Frameworks and declarations of significant training runs.

In the future, these methods may also allow states to verify safety related claims made by other states, including compliance with the safety Assurance Frameworks and declarations of significant training runs.