Advanced AI models hold the promise of tremendous benefits for humanity, but society needs to proactively manage the accompanying risks. In this paper, we focus on what we term "frontier AI" models: highly capable foundation models that could possess dangerous capabilities sufficient to pose severe risks to public safety. Frontier AI models pose a distinct regulatory challenge: dangerous capabilities can arise unexpectedly; it is difficult to robustly prevent a deployed model from being misused; and, it is difficult to stop a model's capabilities from proliferating broadly. To address these challenges, at least three building blocks for the regulation of frontier models are needed: (1) standard-setting processes to identify appropriate requirements for frontier AI developers, (2) registration and reporting requirements to provide regulators with visibility into frontier AI development processes, and (3) mechanisms to ensure compliance with safety standards for the development and deployment of frontier AI models. Industry self-regulation is an important first step. However, wider societal discussions and government intervention will be needed to create standards and to ensure compliance with them. We consider several options to this end, including granting enforcement powers to supervisory authorities and licensure regimes for frontier AI models. Finally, we propose an initial set of safety standards. These include conducting pre-deployment risk assessments; external scrutiny of model behavior; using risk assessments to inform deployment decisions; and monitoring and responding to new information about model capabilities and uses post-deployment. We hope this discussion contributes to the broader conversation on how to balance public safety risks and innovation benefits from advances at the frontier of AI development.

翻译：先进人工智能模型有望为人类带来巨大福祉，但社会需要主动管理随之而来的风险。本文聚焦于我们称之为“前沿人工智能”模型：这些具备高度能力的基石模型可能拥有足以对公共安全构成严重威胁的危险能力。前沿人工智能模型带来了独特的监管挑战：危险能力可能意外涌现；难以稳健地防止已部署模型被滥用；且难以阻止模型能力的广泛扩散。为应对这些挑战，至少需要三项针对前沿模型监管的基础构建：其一，制定标准流程，以确定对前沿人工智能开发者的适当要求；其二，建立注册与报告机制，使监管机构能够洞察前沿人工智能的开发过程；其三，建立确保符合前沿人工智能模型开发与部署安全标准的合规机制。行业自律是重要的第一步，然而，制定标准并确保其合规仍需更广泛的社会讨论与政府干预。为此，我们考虑了多种方案，包括授予监管机构执法权力以及建立前沿人工智能模型的许可制度。最后，我们提出了一套初始安全标准，包括：开展部署前风险评估；对模型行为进行外部审查；利用风险评估指导部署决策；以及监控并响应部署后关于模型能力与用途的新信息。我们希望此讨论能推动更广泛的对话，探讨如何在人工智能前沿发展的进步中平衡公共安全风险与创新效益。