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arXiv · 2507.12713

The Case for Contextual Copyleft: Licensing Open Source Training Data and Generative AI

Grant Shanklin·Emmie Hine·Claudio Novelli·Tyler Schroder·Luciano Floridi
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Abstract

The proliferation of generative AI systems has created new challenges for the Free and Open Source Software (FOSS) community, particularly regarding how traditional copyleft principles should apply when open source code is used to train AI models. This article introduces the Contextual Copyleft AI (CCAI) license, a novel licensing mechanism that extends copyleft requirements from training data to the resulting generative AI models. The CCAI license offers significant advantages, including enhanced developer control, incentivization of open source AI development, and mitigation of openwashing practices. This is demonstrated through a structured three-part evaluation framework that examines (1) legal feasibility under current copyright law, (2) policy justification comparing traditional software and AI contexts, and (3) synthesis of cross-contextual benefits and risks. However, the increased risk profile of open source AI, particularly the potential for direct misuse, necessitates complementary regulatory approaches to achieve an appropriate risk-benefit balance. The paper concludes that when implemented within a robust regulatory environment focused on responsible AI usage, the CCAI license provides a viable mechanism for preserving and adapting core FOSS principles to the evolving landscape of generative AI development.

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Grant Shanklin, Emmie Hine, Claudio Novelli, Tyler Schroder, Luciano Floridi. 2025-07-17. The Case for Contextual Copyleft: Licensing Open Source Training Data and Generative AI. https://doi.org/10.1093/ijlit%2Feaag003

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