Do Not Trust Licenses You See: Dataset Compliance Requires Massive-Scale AI-Powered Lifecycle Tracing

TL;DR

The paper addresses the challenge of legal risk in AI datasets by arguing that license terms alone are insufficient and that tracking the full data lifecycle is essential. It introduces NEXUS and the AutoCompliance AI agent to perform scalable, lifecycle-aware compliance analysis by constructing license dependency graphs and evaluating risk across multiple criteria. The authors demonstrate, via a massive-scale study of 17,429 entities and 8,072 license terms, that redistribution dependencies produce significant, often hidden, legal risks and inversions that surface-only license checks miss. The work establishes a new standard for AI data governance, showing that end-to-end provenance and compliance tracing can improve accuracy, efficiency, and transparency in dataset management and licensing.

Abstract

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.

Figures (11)

• Figure 1: Data Compliance is a multi-layered legal risk assessment framework that evaluates entities through their full data lifecycle. The input includes dataset details such as name, URL, type, modality, and license, which are used to compute a score based on 14 criteria. The score is then used to determine the entity's individual class, and the aggregate class is computed by aggregating the individual classes of all dependencies.
• Figure 2: Overview of AutoCompliance. The user provides the starting web page for the target entity. From the web page, the QA module extracts the information of the target entity, such as name, type, and meta-data. Then, the agent finds the relevant resources on the web to identify the license terms and dependencies. Finally, it uses the target entity information and license terms to evaluate the legal score and individual class.
• Figure 3: Distribution of the types of dependency entities.
• Figure 4: Discrepancies between the individual and aggregate classes of the analyzed entities.
• Figure 5: Inversion occurrences across our 14 criteria.