Legal Compliance in AI Models: A Novel Framework for Analyzing Licensing Risks

Abstract

As AI model development becomes increasingly reliant on integrating datasets, pre-trained models, and opensource components, ensuring license compliance is critical. We present ModelGo, a novel framework designed to detect and resolve licensing conflicts across AI pipelines. Unlike traditional license analysis tools focused solely on software code, ModelGo incorporates a taxonomy tailored to machine learning workflows, enabling fine-grained evaluation of licensing compatibility for datasets, models, and composite artifacts. Our case studies, drawn from real-world scenarios, demonstrate the framework’s effectiveness in identifying licensing risks and guiding developers toward compliant reuse. This work contributes a scalable and extensible approach to managing legal risks in AI systems.