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The case for contextual copyleft: licensing open-source training data and generative AI
Journal article   Open access   Peer reviewed

The case for contextual copyleft: licensing open-source training data and generative AI

Grant Shanklin, Emmie Hine, Claudio Novelli, Tyler Schroder and Luciano Floridi
International journal of law and information technology, Vol.34, 003
01/01/2026

Abstract

Government & Law Law Social Sciences
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.
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https://doi.org/10.1093/ijlit/eaag003View
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