On the Societal Impact of Open Foundation Models
Authors: name: Sayash Kapoor; name: Rishi Bommasani; name: Kevin Klyman; name: Shayne Longpre; name: Ashwin Ramaswami; name: Peter Cihon; name: Aspen Hopkins; name: Kevin Bankston; name: Stella Biderman; name: Miranda Bogen; name: Rumman Chowdhury; name: Alex Engler; name: Peter Henderson; name: Yacine Jernite; name: Seth Lazar; name: Stefano Maffulli; name: Alondra Nelson; name: Joelle Pineau; name: Aviya Skowron; name: Dawn Song; name: Victor Storchan; name: Daniel Zhang; name: Daniel E. Ho; name: Percy Liang; name: Arvind Narayanan
Published: 2024-02-27 16:49:53

Abstract: Foundation models are powerful technologies: how they are released publicly
directly shapes their societal impact. In this position paper, we focus on open
foundation models, defined here as those with broadly available model weights
(e.g. Llama 2, Stable Diffusion XL). We identify five distinctive properties
(e.g. greater customizability, poor monitoring) of open foundation models that
lead to both their benefits and risks. Open foundation models present
significant benefits, with some caveats, that span innovation, competition, the
distribution of decision-making power, and transparency. To understand their
risks of misuse, we design a risk assessment framework for analyzing their
marginal risk. Across several misuse vectors (e.g. cyberattacks, bioweapons),
we find that current research is insufficient to effectively characterize the
marginal risk of open foundation models relative to pre-existing technologies.
The framework helps explain why the marginal risk is low in some cases,
clarifies disagreements about misuse risks by revealing that past work has
focused on different subsets of the framework with different assumptions, and
articulates a way forward for more constructive debate. Overall, our work helps
support a more grounded assessment of the societal impact of open foundation
models by outlining what research is needed to empirically validate their
theoretical benefits and risks.