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

Abstract: The Machine Learning (ML) community has wit- nessed 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 ex- ist 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 noncom- pliance. 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 Hug- ging 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/

Lay Summary:

Machine learning (ML) is rapidly expanding, with millions of models published online and widely reused by developers. When sharing their models, developers must choose a license to define how others can use their work. Common licenses include Apache-2.0, Creative Commons, OpenRAIL, and GPL-3.0. However, there is no widely accepted standard for licensing ML models, which can cause confusion and legal risks.

This paper examines the current state of ML model licensing, comparing popular licenses and identifying the legal uncertainties they create. We analyze real-world examples from Hugging Face to demonstrate how these risks appear in practice. Our findings suggest that current model licensing practices have already led to a quagmire of legal problems for developers and users alike, creating an urgent need for input and attention from the ML community.

We also introduce ModelGo Licenses (MGLs), a new draft license framework designed to promote clearer, more consistent, and legally sound licensing for ML models, fostering a standard and more collaborative ML ecosystem.

Link To Code: https://www.modelgo.li/

Primary Area: System Risks, Safety, and Government Policy

Keywords: License Compliance, AI Governance, Licensing Standards

Submission Number: 110