Frontier AI Regulation: Managing Emerging Risks to Public Safety
Authors: name: Markus Anderljung, name: Joslyn Barnhart, name: Jade Leung, name: Anton Korinek, name: Cullen O'Keefe, name: Jess Whittlestone, name: Shahar Avin, name: Miles Brundage, name: Justin Bullock, name: Duncan Cass-Beggs, name: Ben Chang, name: Tantum Collins, name: Tim Fist, name: Gillian Hadfield, name: Alan Hayes, name: Lewis Ho, name: Sara Hooker, name: Eric Horvitz, name: Noam Kolt, name: Jonas Schuett, name: Yonadav Shavit, name: Divya Siddarth, name: Robert Trager, name: Kevin Wolf

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.