Partially ordering software licenses
This work addresses the lack of systematic comparison for software licenses, benefiting developers and platforms like Hugging Face and GitHub by enabling scalable license analysis.
The authors developed a method using large language models to compare software licenses at scale, constructing a partial ordering based on permissiveness and analyzing license selection through shared provisions. Their approach recovers interpretable attributes corresponding to stricter licenses, with implications for the open-source ecosystem.
Licenses are legal instruments that inventors may use to protect the technologies they build and regulate how they are used -- however, the nature of their authorship and selection means that how they are interpreted, chosen, and enforced is largely unstructured. In practice, this makes it difficult to compare licenses at scale -- when is one license considered more permissive than the other, and when are their terms incomparable to each other? Currently, there is a growing list of licenses that are introduced and used, but there is no systematic way to study their relationships. This matters for platforms such as Hugging Face, GitHub, and the Python Package Index, where developers publish or build upon technologies that each have their own licenses. Using large language models (LLMs), we introduce methods for comparing licenses at scale: first, in a pairwise fashion to construct a partial ordering based on permissiveness, and second, by drawing on existing taxonomies of software licenses. The former allows us to trace restrictiveness, and the latter allows us to understand license selection as a combination of shared provisions. Our analysis recovers certain interpretable attributes that correspond to stricter licenses, with legal implications for the open-source ecosystem.