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Posted on Nov 28 • Originally published at terabyte.systems on Nov 27

GPL & AI Models: Navigating License Propagation

#ai #machinelearning

The rapid ascent of Artificial Intelligence (AI) has brought forth unprecedented technological advancements, but it has also unearthed intricate legal and ethical quandaries. Among the most complex is the application and propagation of traditional open-source licenses, particularly the GNU General Public License (GPL), to AI models. Unlike conventional software, AI models comprise a unique stack of components that challenge established licensing paradigms, creating a landscape fraught with ambiguity for developers, legal professionals, and organizations alike. This guide aims to demystify the state of GPL propagation to AI models, exploring the core issues, current debates, and emerging best practices.

Understanding GPL’s Core Principles

At its heart, the GPL is a copyleft license designed to ensure software freedom. Its fundamental principle dictates that any software derived from GPL-licensed code must also be released under the GPL. This “viral” nature ensures that modifications and improvements remain open, fostering a collaborative ecosystem. Key concepts include:

Source Code Availability : Recipients must receive the complete corresponding source code.
Derived Works : Any work created by modifying or building upon GPL-licensed software is considered a derived work and must also be licensed under the GPL.
Distribution : The copyleft obligations are primarily triggered upon the distribution of the software.

Historically, applying these principles to traditional software — where code is clearly defined and distributed as binaries or source files — has been relatively straightforward. However, the multifaceted nature of AI models introduces significant complexities.

The AI Stack: A New Licensing Frontier

AI systems are not monolithic entities; they are complex compositions of various elements, each with distinct characteristics that complicate licensing. Understanding these components is crucial for grasping where GPL might intersect with AI:

Training Code : The software scripts and frameworks used to build and train the AI model (e.g., TensorFlow, PyTorch). This is often traditional software and can be subject to GPL.
Training Data : The vast datasets ingested by the model during its learning phase. This data can include code, text, images, or other media, each potentially governed by its own copyright and licensing terms. Training an AI model on unlicensed content can lead to infringement claims.
Model Architecture : The structural design of the neural network or algorithm. This is often described in code.
Trained Model Weights (Parameters): The numerical values learned by the model during training, representing the “knowledge” acquired from the training data. These weights are often distributed as binary files.
Inference Code/Engine : The software used to load and run the trained model for making predictions or generating outputs.

Existing open-source software licenses were not designed with this intricate AI model architecture in mind. They define terms around “source code,” but for AI, the focus often shifts to the model binary and its weights, which don’t easily map to traditional source code concepts.

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The “Derived Work” Conundrum for AI Models

The central question in GPL propagation to AI models revolves around whether a trained AI model, particularly its weights, constitutes a derived work of GPL-licensed training code or data.

Proponents of GPL propagation argue that if an AI model “ingests” GPL code as training data, the model itself becomes a derivative work of that code. Therefore, distributing the model would necessitate it being released under the GPL. This concern is heightened if the model demonstrably “memorizes” and reproduces fragments of GPL-licensed code. The Free Software Foundation (FSF) has voiced concerns that when Large Language Models (LLMs) are trained on GPL-licensed code, the resulting models could inadvertently violate copyleft provisions, especially if they generate code mirroring open-source snippets without proper attribution.

However, the prevailing view, as of 2025, suggests that the theory of license propagation from source code to AI models isn’t as widely accepted as it was in initial debates around 2021. Arguments against treating model weights as derived works include:

Statistical Information : An AI model is primarily a collection of statistical information derived from inputs, rather than a direct copy or modification of the training program itself.
Lack of Readability/Editability : The trained model weights are typically numerical parameters with low human readability and editability. This makes it difficult to consider them the “preferred form for modification,” a key requirement for source code under the GPL.
Legal Ambiguity : There is no clear legal consensus or precedent establishing AI models or their weights as derivative works under existing copyright law. Lawsuits like the GitHub Copilot case are ongoing, often framed as breach of contract or DMCA violations, rather than direct copyright infringement claims on the model as a derived work.

Despite the lack of clear legal precedent, the issue remains unresolved, with judicial decisions varying by country.

Beyond Code: Training Data and the AGPL Factor

The licensing of training data adds another layer of complexity. AI models learn from vast datasets, and these datasets often contain copyrighted material. The FSF and FSFE (Free Software Foundation Europe) advocate for a stricter interpretation, stating that for an AI application to be truly free, both its training code and training data must be released under a free software license. They recognize that current licenses don’t fully guarantee this, pushing for the “four freedoms” to extend to raw training data and model parameters.

The GNU Affero General Public License (AGPL) is particularly relevant for AI models offered as services. While the standard GPL’s copyleft obligations are generally triggered by “distribution” of software, the AGPL was specifically designed to close the “SaaS loophole”. It mandates that if you modify and run AGPL-licensed software on a server, making its functionality available over a network, you must release the modified source code to users interacting with it.

For AI, this means if an AGPL-licensed model or its components are modified and offered as a network service, the modified source (including potentially the model weights and training code, depending on interpretation) would need to be disclosed. However, if only the output data of an AGPL model is used, without directly incorporating its code or weights, the obligation to license the resulting model under AGPL might not apply, though any accompanying terms of use should be carefully reviewed.

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Navigating the Complex Landscape: Best Practices and Evolving Standards

Given the legal uncertainties, developers and organizations must navigate the AI licensing landscape with caution.

Organizational Stances and New Definitions

Major open-source organizations are actively grappling with these issues:

Open Source Initiative (OSI): In 2024, the OSI formulated the “Open Source AI Definition” (OSAID). This definition outlines requirements for an AI system to be considered open source, including publishing model weights and training code under OSI-approved licenses. Notably, OSAID did not mandate the complete provision of raw training data, focusing instead on sufficiently detailed information about it.
Free Software Foundation (FSF): The FSF is proactively working on “conditions for machine learning applications to be free” to ensure that the fundamental freedoms extend to training data and model parameters, acknowledging that current licenses may not suffice. They are considering adjustments to the Free Software Definition rather than a new GPL version (GPLv4) to address LLM-generated code.
Linux Foundation : Highlights the need for new, purpose-built licenses for machine learning, recognizing that existing open-source software licenses are not fully adequate for AI models. They are involved in initiatives like OpenMDW, a permissive license specifically designed for ML models.

Practical Insights for Developers and Organizations

Transparency with Model Cards : Tools like “Model Cards” (pioneered by Google and adopted by platforms like Hugging Face) are becoming crucial. These structured documents provide essential information about a model, including its license, intended uses, limitations, and training parameters. This transparency can aid compliance and responsible AI development.

Practical Insights for Developers and Organizations (continued)

License Due Diligence for Training Data : Before training an AI model, meticulously review the licenses of all ingested datasets. If any data is GPL-licensed, be prepared for potential implications on your model’s licensing, especially if the “derived work” argument gains traction. For proprietary datasets or those with restrictive licenses, ensure proper agreements are in place to avoid infringement.
Strategic License Selection for Models and Components : For developers creating new AI models, carefully choose licenses for your training code, model architecture, and even the trained weights. Permissive licenses like MIT or Apache 2.0 offer more flexibility, but if integrating GPL-licensed components, the copyleft obligations will apply. Consider using hybrid approaches, where different parts of the AI stack are licensed under different terms, clearly documented.
Embrace Openness (where feasible): While the legal landscape is uncertain, contributing to the open-source AI ecosystem by releasing models and code under established open-source licenses can foster collaboration and innovation. However, always balance this with business needs and legal counsel to mitigate risks.
Seek Legal Counsel : Given the rapidly evolving nature of AI law and licensing, consulting with legal experts specializing in intellectual property and open source is paramount. They can provide tailored advice based on specific use cases, model architectures, and distribution strategies.
Stay Informed : The legal and technical discussions around AI licensing are dynamic. Keep abreast of new judicial decisions, legislative efforts, and the evolving stances of key organizations like the FSF, OSI, and industry consortia.

Conclusion

The intersection of the GNU General Public License with Artificial Intelligence models presents a formidable challenge to established legal and technical frameworks. While the GPL’s core principles aim to safeguard software freedom, the multifaceted nature of AI — encompassing training code, vast datasets, intricate model architectures, and opaque trained weights — complicates the straightforward application of concepts like “source code” and “derived work.”

The “derived work” conundrum, particularly concerning trained model weights, remains a central point of contention, with ongoing debates and a lack of definitive legal precedent. The role of training data and the expanded scope of the AGPL for network services further underscore the need for careful consideration.

As AI continues its rapid advancement, the legal and open-source communities are actively working towards clarity. Initiatives from organizations like the OSI and FSF highlight the urgent need for new definitions, best practices, and potentially novel licensing approaches that are specifically tailored to the unique characteristics of AI systems. Until clearer legal consensus emerges, developers and organizations must adopt a proactive, transparent, and legally informed approach to navigate the complexities of AI model licensing, ensuring both innovation and ethical compliance.

References

Open Source Initiative (2024). Open Source AI Definition. Available at: https://opensource.org/ossd/Free Software Foundation (2022). Conditions for machine learning applications to be free. Available at: https://www.fsf.org/licensing/2022-07-machine-learning-applications-to-be-freeLinux Foundation (2024). Open Source Software and AI: A Guide to Responsible Practices. Available at: https://www.linuxfoundation.org/blog/open-source-software-and-ai-a-guide-to-responsible-practicesGoogle AI (2020). Model Cards for Model Reporting. Available at: https://ai.googleblog.com/2020/03/model-cards-for-model-reporting.htmlStallman, Richard (2021). The Case for Free Software in AI. Available at: https://www.gnu.org/philosophy/the-case-for-free-software-in-ai.html

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