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.

前沿 AI 模型的安全性规范与公共安全风险需求有关。建立标准设置流程、注册报告需求以及合规机制是对前沿 AI 模型进行规范的必要步骤。产业自律是重要的第一步，但还需要社会广泛讨论和政府干预以确保规范的制定和遵循。将执法权力授予监管机构和颁发前沿 AI 模型许可制度等选项可实现该目标。本文提出一组初始的安全标准，包括进行部署前的风险评估、外部对模型行为的审查、使用风险评估来指导部署决策以及在部署后监控和响应关于模型能力与应用的新信息。希望本文能为如何平衡公共安全风险与 AI 开发前沿的创新受益的广泛讨论做出贡献。