发表机构
Queen’s University（女王大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究人工智能供应链中许可证洗钱问题，通过追踪大量数据集到模型再到应用程序的链，量化两种洗钱形式，发现多数链有无声明许可证工件，各负有义务许可证类别生存率低，为相关方提供了建议。
AI 中文摘要
人工智能工件在多平台供应链中流转，涵盖Hugging Face上的数据集和模型以及GitHub上的应用程序。虽然每个工件都带有许可证，其义务应随重新分发而传播，但尚无研究衡量这些义务在供应链中能否存续，还是会在工件向下游移动时被剥离和替换。我们追踪了232,270条数据集→模型→应用程序链，并量化了两种许可证洗钱形式：无声明许可证的工件在下游获得明确标签，以及在重新分发期间一种声明的许可证类别被另一种取代。我们发现62.3%的链至少经过一个无声明许可证的工件（集中在一小部分基础数据集中），并且每个负有义务的许可证类别端到端生存率低于7%，而宽松类别达到95.1%。基于这些发现，我们为从业者、模型发布者、权利持有者和平台所有者提供了可行的建议。
英文摘要
AI artifacts move through a multi-platform supply chain, spanning datasets and models on Hugging Face and applications on GitHub. While each artifact carries a license whose obligations should propagate through redistribution, no study has yet measured whether those obligations survive the chain or are stripped and replaced as artifacts move downstream. We trace 232,270 dataset$\rightarrow$model$\rightarrow$application chains and quantify two forms of license laundering: when artifacts with no declared license acquire definitive labels downstream, and when one declared license category replaces another during redistribution. We find that 62.3% of chains pass through at least one artifact with no declared license (concentrated in a small set of foundational datasets), and that every obligation-bearing license category falls below 7% end-to-end survival while the Permissive category reaches 95.1%. Based on these findings, we provide actionable recommendations for practitioners, model publishers, rights holders, and platform owners.
Comments9 pages, 2 figures
