Frontier AI Regulation: Managing Emerging Risks to Public Safety
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.
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Markus Anderljung
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2023-07-10T05:42:53.401Z
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Markus Anderljung
user
markusanderljung
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Joslyn Barnhart
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Jade Leung
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Anton Korinek
64ab9a5de831371424249ee3
Cullen O'Keefe
64ab9a5de831371424249ee4
Jess Whittlestone
64ab9a5de831371424249ee5
Shahar Avin
64ab9a5de831371424249ee6
Miles Brundage
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Justin Bullock
64ab9a5de831371424249ee8
Duncan Cass-Beggs
64ab9a5de831371424249ee9
Ben Chang
64ab9a5de831371424249eea
Tantum Collins
64ab9a5de831371424249eeb
Tim Fist
64ab9a5de831371424249eec
Gillian Hadfield
64ab9a5de831371424249eed
Alan Hayes
64ab9a5de831371424249eee
Lewis Ho
64ab9a5de831371424249eef
Sara Hooker
claimed_verified
2024-02-01T08:45:12.249Z
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Sara Hooker
user
sarahooker
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Eric Horvitz
64ab9a5de831371424249ef1
Noam Kolt
64ab9a5de831371424249ef2
Jonas Schuett
64ab9a5de831371424249ef3
Yonadav Shavit
64ab9a5de831371424249ef4
Divya Siddarth
64ab9a5de831371424249ef5
Robert Trager
claimed_verified
2023-07-11T07:54:03.523Z
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Robert Trager
user
rtrager
64ab9a5de831371424249ef6
Kevin Wolf
Frontier AI Regulation: Managing Emerging Risks to Public Safety
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.