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Near to Mid-term Risks and Opportunities of Open-Source Generative AI (2404.17047v2)

Published 25 Apr 2024 in cs.LG
Near to Mid-term Risks and Opportunities of Open-Source Generative AI

Abstract: 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 LLMs. 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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Analysis of Near to Mid-term Risks and Opportunities of Open Source Generative AI

Introduction to the Study

This paper explores the nuanced domain of open sourcing generative AI (GenAI), focusing on its differential impacts over the near to mid-term phase. It begins by clarifying the stages of AI development and proceeds to provide an empirical analysis of the openness of currently available LLMs. Thereafter, it explores the contrasting risks and opportunities presented by open versus closed source AI models. Central to the paper is a compelling argument for the responsible open sourcing of GenAI models, supported by strategic recommendations for approaching this responsibly.

Modeling and Openness Taxonomy

The paper outlines a tripartite development stage for GenAI, classified into near-term, mid-term, and long-term based on technological adoption and capability rather than a fixed timeline. This categorization is pivotal for understanding the distinct operational, ethical, and societal implications at each stage. A significant portion of the analysis is devoted to assessing the current models' openness using an original taxonomy that grades components of AI systems on their openness. This evaluation brings to light a balance between open and closed components, revealing a skew towards more closed training data and safety evaluations.

Risks and Opportunities of Open Source GenAI

The discourse around open source GenAI is ripe with debates on its scalability and implications. The research highlights numerous benefits such as enhanced flexibility, customization potential, and increased transparency leading to greater public trust. However, these come alongside risks such as potential misuse by bad actors and challenges in controlling dissemination once models are publicly released. A nuanced observation provided is that while open source can facilitate innovation and economic inclusivity, it equally necessitates robust mechanisms to mitigate accompanying safety and societal risks.

Dual-Use and Security Concerns

Open source models, despite their inclusivity and potential for rapid proliferation across diverse applications, can be misappropriated to generate unsafe content or be repurposed by malevolent users. The paper emphasizes this dual-use nature as a pivotal concern needing stringent operational checks and community-led oversight to ensure responsible use.

Economic and Academic Impact

Reflections on the potential economic benefits of open source GenAI underscore its capability to democratize AI access, thereby fostering broader global participation in AI development and utilization. In academia, the open-sourcing of models catalyzes more rigorous, diverse research endeavors by providing extensive access to foundational models and datasets.

Recommendations for the Future

Strategic recommendations are presented for fostering a responsible open-source GenAI ecosystem. These include enhancing data transparency, developing robust benchmarks for open evaluation, conducting in-depth security audits, and ongoing assessment of societal impacts. Advocating for an open-source model, the paper suggests these frameworks can mitigate risks while maximizing the technology's positive impacts.

Concluding Thoughts

The paper directs a well-reasoned call towards structured open sourcing of GenAI models in the near to mid-term. By delineating both the optimistic and cautious narratives surrounding open source models, it lays a balanced viewpoint advocating for responsible and strategically planned open sourcing methodologies.

In conclusion, while the paper offers a critical roadmap for navigating the complex terrain of open source GenAI, it equally calls for sustained empirical and theoretical inquiry to adaptively manage emerging challenges and opportunities in this rapidly evolving field.

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Authors (24)
  1. Francisco Eiras (17 papers)
  2. Aleksandar Petrov (21 papers)
  3. Bertie Vidgen (35 papers)
  4. Christian Schroeder de Witt (49 papers)
  5. Fabio Pizzati (22 papers)
  6. Katherine Elkins (5 papers)
  7. Supratik Mukhopadhyay (64 papers)
  8. Adel Bibi (53 papers)
  9. Botos Csaba (5 papers)
  10. Fabro Steibel (2 papers)
  11. Fazl Barez (42 papers)
  12. Genevieve Smith (7 papers)
  13. Gianluca Guadagni (6 papers)
  14. Jon Chun (6 papers)
  15. Jordi Cabot (32 papers)
  16. Joseph Marvin Imperial (28 papers)
  17. Juan A. Nolazco-Flores (2 papers)
  18. Lori Landay (2 papers)
  19. Matthew Jackson (6 papers)
  20. Paul Röttger (37 papers)
Citations (4)
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