title: Frontier AI Regulation: Managing Emerging Risks to Public Safety
summary: 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.
paper_authors._id: 64ab9a5de831371424249edf
paper_authors.name: Markus Anderljung
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paper_authors.fullname: Markus Anderljung
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paper_authors.name: Joslyn Barnhart
paper_authors._id: 64ab9a5de831371424249ee1
paper_authors.name: Jade Leung
paper_authors._id: 64ab9a5de831371424249ee2
paper_authors.name: Anton Korinek
paper_authors._id: 64ab9a5de831371424249ee3
paper_authors.name: Cullen O'Keefe
paper_authors._id: 64ab9a5de831371424249ee4
paper_authors.name: Jess Whittlestone
paper_authors._id: 64ab9a5de831371424249ee5
paper_authors.name: Shahar Avin
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paper_authors.name: Miles Brundage
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paper_authors.name: Justin Bullock
paper_authors._id: 64ab9a5de831371424249ee8
paper_authors.name: Duncan Cass-Beggs
paper_authors._id: 64ab9a5de831371424249ee9
paper_authors.name: Ben Chang
paper_authors._id: 64ab9a5de831371424249eea
paper_authors.name: Tantum Collins
paper_authors._id: 64ab9a5de831371424249eeb
paper_authors.name: Tim Fist
paper_authors._id: 64ab9a5de831371424249eec
paper_authors.name: Gillian Hadfield
paper_authors._id: 64ab9a5de831371424249eed
paper_authors.name: Alan Hayes
paper_authors._id: 64ab9a5de831371424249eee
paper_authors.name: Lewis Ho
paper_authors._id: 64ab9a5de831371424249eef
paper_authors.name: Sara Hooker
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paper_authors.fullname: Sara Hooker
paper_authors.type: user
paper_authors.user: sarahooker
paper_authors._id: 64ab9a5de831371424249ef0
paper_authors.name: Eric Horvitz
paper_authors._id: 64ab9a5de831371424249ef1
paper_authors.name: Noam Kolt
paper_authors._id: 64ab9a5de831371424249ef2
paper_authors.name: Jonas Schuett
paper_authors._id: 64ab9a5de831371424249ef3
paper_authors.name: Yonadav Shavit
paper_authors._id: 64ab9a5de831371424249ef4
paper_authors.name: Divya Siddarth
paper_authors._id: 64ab9a5de831371424249ef5
paper_authors.name: Robert Trager
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-11T07:54:03.523Z
paper_authors._id: 64abdf570e2e93502a68c3ce
paper_authors.avatarUrl: /avatars/f86e6ae7a8797206931256d0873fd37c.svg
paper_authors.fullname: Robert Trager
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paper_authors.user: rtrager
paper_authors._id: 64ab9a5de831371424249ef6
paper_authors.name: Kevin Wolf
paper_title: Frontier AI Regulation: Managing Emerging Risks to Public Safety
paper_summary: 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.