A Different Approach to AI Safety: Proceedings from the Columbia Convening on Openness in Artificial Intelligence and AI Safety
Citations
0
Open access
No
Source
arxiv
OpenAlex
Not enriched
arXiv
2506.22183
Abstract
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.
Collections
Add to collection
Paper intelligence
Analysis has not been completed yet.
No graph connections yet.
Sync citations or add papers to shared collections to build this network.
Knowledge graph
Citation network
Explore references, papers that cite this work and related papers in your Codex library.
References
0No references have been linked yet.
Cited by
0No saved paper is currently linked as citing this work.
Related papers
0Add papers to shared collections or enrich their topics to find related work.
Research workspace
Attach the paper PDF, extract its text, classify its contents and create semantic embeddings.
Paper resources
No PDF assets have been attached to this paper yet.