title: Open-Sourcing Highly Capable Foundation Models: An evaluation of risks,
    benefits, and alternative methods for pursuing open-source objectives
summary: Recent decisions by leading AI labs to either open-source their models or to
  restrict access to their models has sparked debate about whether, and how,
  increasingly capable AI models should be shared. Open-sourcing in AI typically
  refers to making model architecture and weights freely and publicly accessible
  for anyone to modify, study, build on, and use. This offers advantages such as
  enabling external oversight, accelerating progress, and decentralizing control
  over AI development and use. However, it also presents a growing potential for
  misuse and unintended consequences. This paper offers an examination of the
  risks and benefits of open-sourcing highly capable foundation models. While
  open-sourcing has historically provided substantial net benefits for most
  software and AI development processes, we argue that for some highly capable
  foundation models likely to be developed in the near future, open-sourcing may
  pose sufficiently extreme risks to outweigh the benefits. In such a case,
  highly capable foundation models should not be open-sourced, at least not
  initially. Alternative strategies, including non-open-source model sharing
  options, are explored. The paper concludes with recommendations for developers,
  standard-setting bodies, and governments for establishing safe and responsible
  model sharing practices and preserving open-source benefits where safe.
paper_authors._id: 6556ea3747c93d37a888a853
paper_authors.name: Elizabeth Seger
paper_authors._id: 6556ea3747c93d37a888a854
paper_authors.name: Noemi Dreksler
paper_authors.status: admin_assigned
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paper_authors.name: Richard Moulange
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paper_authors.name: Emily Dardaman
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paper_authors.name: Jonas Schuett
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paper_authors.fullname: Jonas Schuett
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paper_authors.user: jonasschuett
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paper_authors.name: K. Wei
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paper_authors.name: Christoph Winter
paper_authors.status: admin_assigned
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paper_authors.fullname: Christoph Winter
paper_authors.type: user
paper_authors.user: chrihech
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paper_authors.name: Mackenzie Arnold
paper_authors._id: 6556ea3747c93d37a888a85b
paper_authors.name: Seán Ó hÉigeartaigh
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paper_authors.name: Anton Korinek
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paper_authors.name: Markus Anderljung
paper_authors.status: admin_assigned
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paper_authors._id: 64624f222a83863b97bda981
paper_authors.avatarUrl: /avatars/bd13153c1358d6cdcb7959478ef5f3e1.svg
paper_authors.fullname: Markus Anderljung
paper_authors.type: user
paper_authors.user: markusanderljung
paper_authors._id: 6556ea3747c93d37a888a85e
paper_authors.name: Ben Bucknall
paper_authors._id: 6556ea3747c93d37a888a85f
paper_authors.name: Alan Chan
paper_authors._id: 6556ea3747c93d37a888a860
paper_authors.name: Eoghan Stafford
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-12-05T08:41:45.625Z
paper_authors._id: 656e8be8a2076764c8d4f354
paper_authors.avatarUrl: /avatars/528fd1296774101c5aba701269efd0dd.svg
paper_authors.fullname: Eoghan Stafford
paper_authors.type: user
paper_authors.user: EoghanStafford
paper_authors._id: 6556ea3747c93d37a888a861
paper_authors.name: Leonie Koessler
paper_authors._id: 6556ea3747c93d37a888a862
paper_authors.name: Aviv Ovadya
paper_authors._id: 6556ea3747c93d37a888a863
paper_authors.name: Ben Garfinkel
paper_authors._id: 6556ea3747c93d37a888a864
paper_authors.name: Emma Bluemke
paper_authors._id: 6556ea3747c93d37a888a865
paper_authors.name: Michael Aird
paper_authors._id: 6556ea3747c93d37a888a866
paper_authors.name: Patrick Levermore
paper_authors._id: 6556ea3747c93d37a888a867
paper_authors.name: Julian Hazell
paper_authors._id: 6556ea3747c93d37a888a868
paper_authors.name: Abhishek Gupta
paper_title: Open-Sourcing Highly Capable Foundation Models: An evaluation of risks,
    benefits, and alternative methods for pursuing open-source objectives
paper_summary: Recent decisions by leading AI labs to either open-source their models or to
  restrict access to their models has sparked debate about whether, and how,
  increasingly capable AI models should be shared. Open-sourcing in AI typically
  refers to making model architecture and weights freely and publicly accessible
  for anyone to modify, study, build on, and use. This offers advantages such as
  enabling external oversight, accelerating progress, and decentralizing control
  over AI development and use. However, it also presents a growing potential for
  misuse and unintended consequences. This paper offers an examination of the
  risks and benefits of open-sourcing highly capable foundation models. While
  open-sourcing has historically provided substantial net benefits for most
  software and AI development processes, we argue that for some highly capable
  foundation models likely to be developed in the near future, open-sourcing may
  pose sufficiently extreme risks to outweigh the benefits. In such a case,
  highly capable foundation models should not be open-sourced, at least not
  initially. Alternative strategies, including non-open-source model sharing
  options, are explored. The paper concludes with recommendations for developers,
  standard-setting bodies, and governments for establishing safe and responsible
  model sharing practices and preserving open-source benefits where safe.
paper_ai_summary: The paper discusses the risks and benefits of open-sourcing highly capable AI foundation models and recommends strategies for safe and responsible model sharing.