Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance

Moming Duan, Mingzhe Du, Rui Zhao, Mengying Wang, Yinghui Wu, Nigel Shadbolt, Bingsheng He
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:81276-81291, 2025.

Abstract

The Machine Learning (ML) community has witnessed explosive growth, with millions of ML models being published on the Web. Reusing ML model components has been prevalent nowadays. Developers are often required to choose a license to publish and govern the use of their models. Popular options include Apache-2.0, OpenRAIL (Responsible AI Licenses), Creative Commons Licenses (CCs), Llama2, and GPL-3.0. Currently, no standard or widely accepted best practices exist for model licensing. But does this lack of standardization lead to undesired consequences? Our answer is Yes. After reviewing the clauses of the most widely adopted licenses, we take the position that current model licensing practices are dragging us into a quagmire of legal noncompliance. To support this view, we explore the cur- rent practices in model licensing and highlight the differences between various model licenses. We then identify potential legal risks associated with these licenses and demonstrate these risks using examples from real-world repositories on Hugging Face. To foster a more standardized future for model licensing, we also propose a new draft of model licenses, ModelGo Licenses (MGLs), to address these challenges and promote better compliance. https://www.modelgo.li/

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-duan25d, title = {Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance}, author = {Duan, Moming and Du, Mingzhe and Zhao, Rui and Wang, Mengying and Wu, Yinghui and Shadbolt, Nigel and He, Bingsheng}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {81276--81291}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/duan25d/duan25d.pdf}, url = {https://proceedings.mlr.press/v267/duan25d.html}, abstract = {The Machine Learning (ML) community has witnessed explosive growth, with millions of ML models being published on the Web. Reusing ML model components has been prevalent nowadays. Developers are often required to choose a license to publish and govern the use of their models. Popular options include Apache-2.0, OpenRAIL (Responsible AI Licenses), Creative Commons Licenses (CCs), Llama2, and GPL-3.0. Currently, no standard or widely accepted best practices exist for model licensing. But does this lack of standardization lead to undesired consequences? Our answer is Yes. After reviewing the clauses of the most widely adopted licenses, we take the position that current model licensing practices are dragging us into a quagmire of legal noncompliance. To support this view, we explore the cur- rent practices in model licensing and highlight the differences between various model licenses. We then identify potential legal risks associated with these licenses and demonstrate these risks using examples from real-world repositories on Hugging Face. To foster a more standardized future for model licensing, we also propose a new draft of model licenses, ModelGo Licenses (MGLs), to address these challenges and promote better compliance. https://www.modelgo.li/} }
Endnote
%0 Conference Paper %T Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance %A Moming Duan %A Mingzhe Du %A Rui Zhao %A Mengying Wang %A Yinghui Wu %A Nigel Shadbolt %A Bingsheng He %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-duan25d %I PMLR %P 81276--81291 %U https://proceedings.mlr.press/v267/duan25d.html %V 267 %X The Machine Learning (ML) community has witnessed explosive growth, with millions of ML models being published on the Web. Reusing ML model components has been prevalent nowadays. Developers are often required to choose a license to publish and govern the use of their models. Popular options include Apache-2.0, OpenRAIL (Responsible AI Licenses), Creative Commons Licenses (CCs), Llama2, and GPL-3.0. Currently, no standard or widely accepted best practices exist for model licensing. But does this lack of standardization lead to undesired consequences? Our answer is Yes. After reviewing the clauses of the most widely adopted licenses, we take the position that current model licensing practices are dragging us into a quagmire of legal noncompliance. To support this view, we explore the cur- rent practices in model licensing and highlight the differences between various model licenses. We then identify potential legal risks associated with these licenses and demonstrate these risks using examples from real-world repositories on Hugging Face. To foster a more standardized future for model licensing, we also propose a new draft of model licenses, ModelGo Licenses (MGLs), to address these challenges and promote better compliance. https://www.modelgo.li/
APA
Duan, M., Du, M., Zhao, R., Wang, M., Wu, Y., Shadbolt, N. & He, B.. (2025). Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:81276-81291 Available from https://proceedings.mlr.press/v267/duan25d.html.

Related Material