Several jurisdictions are starting to regulate frontier artificial intelligence (AI) systems, i.e. general-purpose AI systems that match or exceed the capabilities present in the most advanced systems. To reduce risks from these systems, regulators may require frontier AI developers to adopt safety measures. The requirements could be formulated as high-level principles (e.g. 'AI systems should be safe and secure') or specific rules (e.g. 'AI systems must be evaluated for dangerous model capabilities following the protocol set forth in...'). These regulatory approaches, known as 'principle-based' and 'rule-based' regulation, have complementary strengths and weaknesses. While specific rules provide more certainty and are easier to enforce, they can quickly become outdated and lead to box-ticking. Conversely, while high-level principles provide less certainty and are more costly to enforce, they are more adaptable and more appropriate in situations where the regulator is unsure exactly what behavior would best advance a given regulatory objective. However, rule-based and principle-based regulation are not binary options. Policymakers must choose a point on the spectrum between them, recognizing that the right level of specificity may vary between requirements and change over time. We recommend that policymakers should initially (1) mandate adherence to high-level principles for safe frontier AI development and deployment, (2) ensure that regulators closely oversee how developers comply with these principles, and (3) urgently build up regulatory capacity. Over time, the approach should likely become more rule-based. Our recommendations are based on a number of assumptions, including (A) risks from frontier AI systems are poorly understood and rapidly evolving, (B) many safety practices are still nascent, and (C) frontier AI developers are best placed to innovate on safety practices.

翻译：多个司法管辖区正着手监管前沿人工智能系统，即那些能力匹配或超越现有最先进系统的通用人工智能系统。为降低此类系统带来的风险，监管机构可能要求前沿人工智能开发者采取安全措施。相关要求可被表述为高层级原则（例如“人工智能系统应安全可靠”）或具体规则（例如“必须按照……规定的协议评估人工智能系统的危险模型能力”）。这两种被称为“基于原则”与“基于规则”的监管方式具有互补的优缺点。具体规则虽能提供更高确定性且更易执行，但可能迅速过时并引发形式合规问题；反之，高层级原则虽确定性较低且执行成本更高，却更具适应性，更适用于监管机构不确定何种行为最能实现特定监管目标的情境。然而，规则导向与原则导向并非二元对立的选择。政策制定者必须在两者构成的谱系中选择定位点，并认识到具体化程度可能因不同要求而异，并随时间动态演变。我们建议政策制定者应首先（1）强制要求遵循前沿人工智能开发与部署的高层级安全原则，（2）确保监管机构密切监督开发者对这些原则的遵守情况，（3）紧急构建监管能力。随着时间推移，监管方式应逐步向规则导向演进。我们的建议基于若干前提假设，包括：（A）前沿人工智能系统的风险认知尚不充分且快速演变，（B）众多安全实践仍处于萌芽阶段，（C）前沿人工智能开发者最具备开展安全实践创新的条件。