AI追踪数据全生命周期，才能真正看清版权风险。
Do Not Trust Licenses You See: Dataset Compliance Requires Massive-Scale AI-Powered Lifecycle Tracing
- 用AI自动追踪数据从源到分发的完整路径
- 仅21%可商用数据集实际符合许可条款
- 适合关注数据合规与法律风险的团队
本文指出，仅凭数据集许可证无法准确评估其法律风险，必须追踪数据的完整生命周期。传统人工方式难以在大规模下完成数据溯源、权利验证和动态风险评估。为此，我们构建了名为NEXUS的自动化合规系统，利用AI实现高效精准的数据合规分析。通过对17,429个实体和8,072份许可证的大规模分析发现，尽管有2,852个数据集具备商业许可条款，但仅有605个（21%）真正允许商业化使用。该研究确立了以全生命周期视角进行数据治理的新标准，推动透明、合法、负责任的数据管理。
原文摘要 · Abstract (English)
This paper argues that a dataset's legal risk cannot be accurately assessed by its license terms alone; instead, tracking dataset redistribution and its full lifecycle is essential. However, this process is too complex for legal experts to handle manually at scale. Tracking dataset provenance, verifying redistribution rights, and assessing evolving legal risks across multiple stages require a level of precision and efficiency that exceeds human capabilities. Addressing this challenge effectively demands AI agents that can systematically trace dataset redistribution, analyze compliance, and identify legal risks. We develop an automated data compliance system called NEXUS and show that AI can perform these tasks with higher accuracy, efficiency, and cost-effectiveness than human experts. Our massive legal analysis of 17,429 unique entities and 8,072 license terms using this approach reveals the discrepancies in legal rights between the original datasets before redistribution and their redistributed subsets, underscoring the necessity of the data lifecycle-aware compliance. For instance, we find that out of 2,852 datasets with commercially viable individual license terms, only 605 (21%) are legally permissible for commercialization. This work sets a new standard for AI data governance, advocating for a framework that systematically examines the entire lifecycle of dataset redistribution to ensure transparent, legal, and responsible dataset management.
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