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arxiv: 2405.08597 · v3 · pith:T47TJAKM · submitted 2024-05-14 · cs.LG

Risks and Opportunities of Open-Source Generative AI

Francisco Eiras , Aleksandar Petrov , Bertie Vidgen , Christian Schroeder , Fabio Pizzati , Katherine Elkins , Supratik Mukhopadhyay , Adel Bibi
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Aaron Purewal Csaba Botos Fabro Steibel Fazel Keshtkar Fazl Barez Genevieve Smith Gianluca Guadagni Jon Chun Jordi Cabot Joseph Imperial Juan Arturo Nolazco Lori Landay Matthew Jackson Phillip H. S. Torr Trevor Darrell Yong Lee Jakob Foerster
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classification cs.LG
keywords generativeopen-sourceriskscapabilitiesdevelopmentlong-termmodelsnear
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Applications of Generative AI (Gen AI) are expected to revolutionize a number of different areas, ranging from science & medicine to education. The potential for these seismic changes has triggered a lively debate about the potential risks of the technology, and resulted in calls for tighter regulation, in particular from some of the major tech companies who are leading in AI development. This regulation is likely to put at risk the budding field of open-source generative AI. Using a three-stage framework for Gen AI development (near, mid and long-term), we analyze the risks and opportunities of open-source generative AI models with similar capabilities to the ones currently available (near to mid-term) and with greater capabilities (long-term). We argue that, overall, the benefits of open-source Gen AI outweigh its risks. As such, we encourage the open sourcing of models, training and evaluation data, and provide a set of recommendations and best practices for managing risks associated with open-source generative AI.

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