Open-Sourcing Highly Capable Foundation Models: An evaluation of risks,
  benefits, and alternative methods for pursuing open-source objectives
Authors: name: Elizabeth Seger, name: Noemi Dreksler, name: Richard Moulange, name: Emily Dardaman, name: Jonas Schuett, name: K. Wei, name: Christoph Winter, name: Mackenzie Arnold, name: Seán Ó hÉigeartaigh, name: Anton Korinek, name: Markus Anderljung, name: Ben Bucknall, name: Alan Chan, name: Eoghan Stafford, name: Leonie Koessler, name: Aviv Ovadya, name: Ben Garfinkel, name: Emma Bluemke, name: Michael Aird, name: Patrick Levermore, name: Julian Hazell, name: Abhishek Gupta

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.