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On the Societal Impact of Open Foundation Models

Date
2024
Author(s)
Kapoor, Sayash
Bommasani, Rishi
Klyman, Kevin
Longpre, Shayne
Ramaswami, Ashwin
Cihon, Peter
Hopkins, Aspen
Bankston, Kevin
Biderman, Stella
Bogen, Miranda
Chowdhury, Rumman
Engler, Alex
Henderson, Peter
Jernite, Yacine
Lazar, Seth
Maffulli, Stefano
Nelson, Alondra
Pineau, Joelle
Skowron, Aviya
Song, Dawn
Storchan, Victor
Zhang, Daniel
Ho, Daniel E.
Liang, Percy
Narayanan, Arvind
URI
https://albert.ias.edu/20.500.12111/8220
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 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.
Subjects
open foundation models
societal impact
machine learning
artificial intelligence
sociotechnical
marginal risk
Description
Kapoor, S., R. Bommasani, K. Klyman, et al., On the Societal Impact of Open Foundation Models, Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria. PMLR 235, 2024. Copyright 2024 by the author(s).
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Kapoor-Bommasani-Klyman-etal_On-Societal-Impact-Open-Foundation-Models_2024.pdf

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Description
Kapoor, S., R. Bommasani, K. Klyman, et al., On the Societal Impact of Open Foundation Models, Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria. PMLR 235, 2024. Copyright 2024 by the author(s).
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