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| Main Authors: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.22183 |
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| _version_ | 1866912453764317184 |
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| author | François, Camille Péran, Ludovic Bdeir, Ayah Dziri, Nouha Hawkins, Will Jernite, Yacine Kapoor, Sayash Shen, Juliet Khlaaf, Heidy Klyman, Kevin Marda, Nik Pellat, Marie Raji, Deb Siddarth, Divya Skowron, Aviya Spisak, Joseph Srikumar, Madhulika Storchan, Victor Tang, Audrey Weedon, Jen |
| author_facet | François, Camille Péran, Ludovic Bdeir, Ayah Dziri, Nouha Hawkins, Will Jernite, Yacine Kapoor, Sayash Shen, Juliet Khlaaf, Heidy Klyman, Kevin Marda, Nik Pellat, Marie Raji, Deb Siddarth, Divya Skowron, Aviya Spisak, Joseph Srikumar, Madhulika Storchan, Victor Tang, Audrey Weedon, Jen |
| contents | The rapid rise of open-weight and open-source foundation models is intensifying the obligation and reshaping the opportunity to make AI systems safe. This paper reports outcomes from the Columbia Convening on AI Openness and Safety (San Francisco, 19 Nov 2024) and its six-week preparatory programme involving more than forty-five researchers, engineers, and policy leaders from academia, industry, civil society, and government. Using a participatory, solutions-oriented process, the working groups produced (i) a research agenda at the intersection of safety and open source AI; (ii) a mapping of existing and needed technical interventions and open source tools to safely and responsibly deploy open foundation models across the AI development workflow; and (iii) a mapping of the content safety filter ecosystem with a proposed roadmap for future research and development. We find that openness -- understood as transparent weights, interoperable tooling, and public governance -- can enhance safety by enabling independent scrutiny, decentralized mitigation, and culturally plural oversight. However, significant gaps persist: scarce multimodal and multilingual benchmarks, limited defenses against prompt-injection and compositional attacks in agentic systems, and insufficient participatory mechanisms for communities most affected by AI harms. The paper concludes with a roadmap of five priority research directions, emphasizing participatory inputs, future-proof content filters, ecosystem-wide safety infrastructure, rigorous agentic safeguards, and expanded harm taxonomies. These recommendations informed the February 2025 French AI Action Summit and lay groundwork for an open, plural, and accountable AI safety discipline. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_22183 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Different Approach to AI Safety: Proceedings from the Columbia Convening on Openness in Artificial Intelligence and AI Safety François, Camille Péran, Ludovic Bdeir, Ayah Dziri, Nouha Hawkins, Will Jernite, Yacine Kapoor, Sayash Shen, Juliet Khlaaf, Heidy Klyman, Kevin Marda, Nik Pellat, Marie Raji, Deb Siddarth, Divya Skowron, Aviya Spisak, Joseph Srikumar, Madhulika Storchan, Victor Tang, Audrey Weedon, Jen Artificial Intelligence The rapid rise of open-weight and open-source foundation models is intensifying the obligation and reshaping the opportunity to make AI systems safe. This paper reports outcomes from the Columbia Convening on AI Openness and Safety (San Francisco, 19 Nov 2024) and its six-week preparatory programme involving more than forty-five researchers, engineers, and policy leaders from academia, industry, civil society, and government. Using a participatory, solutions-oriented process, the working groups produced (i) a research agenda at the intersection of safety and open source AI; (ii) a mapping of existing and needed technical interventions and open source tools to safely and responsibly deploy open foundation models across the AI development workflow; and (iii) a mapping of the content safety filter ecosystem with a proposed roadmap for future research and development. We find that openness -- understood as transparent weights, interoperable tooling, and public governance -- can enhance safety by enabling independent scrutiny, decentralized mitigation, and culturally plural oversight. However, significant gaps persist: scarce multimodal and multilingual benchmarks, limited defenses against prompt-injection and compositional attacks in agentic systems, and insufficient participatory mechanisms for communities most affected by AI harms. The paper concludes with a roadmap of five priority research directions, emphasizing participatory inputs, future-proof content filters, ecosystem-wide safety infrastructure, rigorous agentic safeguards, and expanded harm taxonomies. These recommendations informed the February 2025 French AI Action Summit and lay groundwork for an open, plural, and accountable AI safety discipline. |
| title | A Different Approach to AI Safety: Proceedings from the Columbia Convening on Openness in Artificial Intelligence and AI Safety |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2506.22183 |