# From Hugging Face to GitHub: Tracing License Drift in the Open-Source AI Ecosystem

arXiv: https://arxiv.org/abs/2509.09873 · HTML: https://mlsanity.com/paper/2509.09873/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 6 references that are in the map.
- First posted on arXiv 11 Sep 2025.

## Builds on (most cited first, 1 of 1)

- [Model Hubs and Beyond: Analyzing Model Popularity, Performance, and Documentation](https://mlsanity.com/paper/2503.15222/) (2025)

## Built on by (most cited first, 1 of 1)

- [A governance horizon for ethical-use constraints in open-weight AI models](https://mlsanity.com/paper/2605.24383/) (2026)

## Abstract (arXiv)

Hidden license conflicts in the open-source AI ecosystem pose serious legal and ethical risks, exposing organizations to potential litigation and users to undisclosed risk. However, the field lacks a data-driven understanding of how frequently these conflicts occur, where they originate, and which communities are most affected. We present the first end-to-end audit of licenses for datasets and models on Hugging Face, as well as their downstream integration into open-source software applications, covering 364 thousand datasets, 1.6 million models, and 140 thousand GitHub projects. Our empirical analysis reveals systemic non-compliance in which 35.5% of model-to-application transitions eliminate restrictive license clauses by relicensing under permissive terms. In addition, we prototype an extensible rule engine that encodes almost 200 SPDX and model-specific clauses for detecting license conflicts, which can solve 86.4% of license conflicts in software applications. To support future research, we release our dataset and the prototype engine. Our study highlights license compliance as a critical governance challenge in open-source AI and provides both the data and tools necessary to enable automated, AI-aware compliance at scale.