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Near to Mid-term Risks and Opportunities of Open-Source Generative AI

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arxiv 2404.17047 v2 pith:YJDE4UDO submitted 2024-04-25 cs.LG

Near to Mid-term Risks and Opportunities of Open-Source Generative AI

classification cs.LG
keywords generativenearpotentialriskscallscurrentmid-termmodels
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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In the next few years, applications of Generative 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 potential risks 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. We argue for the responsible open sourcing of generative AI models in the near and medium term. To set the stage, we first introduce an AI openness taxonomy system and apply it to 40 current large language models. We then outline differential benefits and risks of open versus closed source AI and present potential risk mitigation, ranging from best practices to calls for technical and scientific contributions. We hope that this report will add a much needed missing voice to the current public discourse on near to mid-term AI safety and other societal impact.

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Cited by 2 Pith papers

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  1. Open AI in the Wild: Adoption and Adaptation of Open Models on r/LocalLLaMA

    cs.HC 2026-06 unverdicted novelty 6.0

    Thematic analysis of r/LocalLLaMA discussions finds users define openness via reliability, local control, privacy, and adaptation under compute, licensing, and usability constraints.

  2. Why Open Source? A Game-Theoretic Analysis of the AI Race

    cs.GT 2026-04 unverdicted novelty 6.0

    A game-theoretic R&D race model shows that pure Nash equilibria for open-sourcing decisions exist and are computationally tractable in both discrete and continuous settings.