The case for contextual copyleft: licensing open-source training data and generative AI
Outlet Title
International Journal of Law and Information Technology
Document Type
Article
Publication Date
2026
Abstract
The rise of generative AI systems presents new challenges for the Free and Open-Source Software (FOSS) community, particularly around applying copyleft principles when open-source code is used to train AI models. This article introduces the Contextual Copyleft AI (CCAI) licence, a novel use of the copyleft mechanism that extends licence obligations from training data to resulting generative models. The CCAI licence enhances developer control, incentivizes open-source AI, and mitigates open-washing. A structured three-part evaluation examines: (i) legal feasibility under current copyright law, (ii) policy justification across traditional software and AI, and (iii) cross-contextual benefits and risks. Still, open-source AI carries a higher risk—especially misuse—making complementary regulation essential to achieve a fair risk-benefit balance. The article concludes that, within a robust regulatory environment focused on responsible AI, the CCAI licence offers a viable path for preserving and adapting core FOSS values to meet the demands of modern AI development.
Recommended Citation
Shanklin, Grant; Hine, Emmie; Novelli, Claudio; Schroder, Tyler; and Floridi, Luciano, "The case for contextual copyleft: licensing open-source training data and generative AI" (2026). Research & Publications. 131.
https://scholar.dsu.edu/ccspapers/131