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

International Conference on Machine Learning 2025 · Oral

In an era increasingly defined by the rapid proliferation and pervasive reuse of **foundation models**, the legal landscape governing their deployment and derivative works is becoming alarmingly complex and fraught with peril. This talk, presented by Mengying Wang from Case Western Reserve University, on behalf of lead author Moming Duan and their collaborators, directly confronts the burgeoning issue of **legal noncompliance** stemming from current model licensing practices. The core assertion is that the existing licensing frameworks, often borrowed from software or general content licenses, are ill-suited for the unique characteristics and intricate reuse patterns prevalent in the machine learning ecosystem, particularly on platforms like Hugging Face.

AI review

This talk identifies a real and underappreciated problem — that model licensing on platforms like Hugging Face is a mess, and that generic software licenses map poorly onto the semantics of model weights, fine-tuning, and distillation — but it does not deliver the intellectual infrastructure needed to actually solve that problem. The empirical audit is descriptive rather than analytic, the proposed ModelGo license is presented without rigorous legal or formal justification for its design choices, and the headline statistic ('more than half of qualified models suffer from license conflicts')…