TY - JOUR
T1 - Position
T2 - 41st International Conference on Machine Learning, ICML 2024
AU - Kapoor, Sayash
AU - Bommasani, Rishi
AU - Klyman, Kevin
AU - Longpre, Shayne
AU - Ramaswami, Ashwin
AU - Cihon, Peter
AU - Hopkins, Aspen
AU - Bankston, Kevin
AU - Biderman, Stella
AU - Bogen, Miranda
AU - Chowdhury, Rumman
AU - Engler, Alex
AU - Henderson, Peter
AU - Jernite, Yacine
AU - Lazar, Seth
AU - Maffulli, Stefano
AU - Nelson, Alondra
AU - Pineau, Joelle
AU - Skowron, Aviya
AU - Song, Dawn
AU - Storchan, Victor
AU - Zhang, Daniel
AU - Ho, Daniel E.
AU - Liang, Percy
AU - Narayanan, Arvind
N1 - Publisher Copyright:
Copyright 2024 by the author(s)
PY - 2024
Y1 - 2024
N2 - 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 3, Stable Diffusion XL). We identify five distinctive properties of open foundation models (e.g. greater customizability, poor monitoring) that mediate 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 supports 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.
AB - 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 3, Stable Diffusion XL). We identify five distinctive properties of open foundation models (e.g. greater customizability, poor monitoring) that mediate 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 supports 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.
UR - http://www.scopus.com/inward/record.url?scp=85203834338&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85203834338&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85203834338
SN - 2640-3498
VL - 235
SP - 23082
EP - 23104
JO - Proceedings of Machine Learning Research
JF - Proceedings of Machine Learning Research
Y2 - 21 July 2024 through 27 July 2024
ER -