commit 39e1cfb2d20d7e231684659371ee41cdc9a4c14c equwal <13551856+equwal@users.noreply.github.com> 2026-10-05 02:20:08 -0700 Revert CCAI clause: F-Droid app keeps AGPL-3.0-only
LICENSE | 11 ----------- ccai-2507.12713.pdf | Bin 327583 -> 0 bytes 2 files changed, 11 deletions(-)
diff --git a/LICENSE b/LICENSE index 6573940..fe6b903 100644 --- a/LICENSE +++ b/LICENSE @@ -660,14 +660,3 @@ if any, to sign a "copyright disclaimer" for the program, if necessary. For more information on this, and how to apply and follow the GNU AGPL, see <https://www.gnu.org/licenses/>. -Contextual Copyleft AI (CCAI) Clause -Added 2026-10-04. -If this software is used to train, fine-tune, or otherwise generate any machine learning model or generative AI -system, any resulting model, dataset, or system must be released under terms no more restrictive than this license. -This includes the requirement to make available the training code, a description of the data used in training, and -the model’s parameters and architecture. Providing access to such a model or its outputs via a network is -considered distribution and triggers this obligation. - -Paper: https://arxiv.org/pdf/2507.12713 -Mirror: https://dickt.store/fuckai.pdf -Copy: ccai-2507.12713.pdf (Shanklin, Hine, Novelli, Schroder and Floridi, CC BY-NC-SA 4.0) diff --git a/ccai-2507.12713.pdf b/ccai-2507.12713.pdf deleted file mode 100644 index 7d50d46..0000000 --- a/ccai-2507.12713.pdf +++ /dev/null @@ -1,693 +0,0 @@ -The Case for Contextual Copyleft: Licensing Open Source Training Data and Generative AI - -Grant Shanklin1,2, Emmie Hine1,3,4, Claudio Novelli1, Tyler Schroder1,2, Luciano Floridi1,3 - -1 Digital Ethics Center, Yale University, 85 Trumbull St, New Haven, CT 06511 -2 Department of Computer Science, Yale University, 51 Prospect St, New Haven, CT 06511 -3 Department of Legal Studies, University of Bologna, Via Zamboni 27/29, 40126 Bologna, Italy -4 Center for IT & IP Law, KU Leuven, Sint-Michielsstraat, 6 30000 Leuven, BE - -Email for correspondence: grant.shanklin@yale.edu - -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 open- -washing 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. - -Keywords -Copyleft, Open source, Free Software, Generative AI, Responsible AI - - 1 - 1. Introduction -With the recent increase in the number (Gozalo-Brizuela and Garrido-Merchan 2023) and power -(Bandi, Adapa, and Kuchi 2023) of generative AI systems, the open source community faces new -challenges in responding effectively. There is considerable discussion regarding the adoption of open -source generative AI models, particularly given the substantial opportunities and risks that accompany -making these systems open (Eiras, Petrov, Vidgen, Schroeder, et al. 2024; Floridi et al. 2025). Some -participants in the debate suggest shifting the focus away from open source generative AI, emphasizing -the need for responsible AI through behavioral use licensing instead (Contractor et al. 2022). -Questions persist about how the open source community should manage models trained on open -source code (a practice notably illustrated by GitHub’s CoPilot, as discussed in Section 4.2). This -dialogue focuses on preserving and adapting the rules, principles, and practices of the Free and Open -Source Software (FOSS) movement to navigate the evolving technical landscape introduced by -generative AI. - - One way the FOSS movement has historically protected free software is through “copyleft” -licenses. These licenses require that derivative works be distributed under the same open source license -as the original work. This safeguards free and open source software because copyleft propagates -licenses from source material to derivative works, creating a viral effect where both original and -derivative works remain free and open (Phillips 2007). If attributes such as “freedom” and “openness” -are desirable for generative AI systems, then copyleft may provide a promising mechanism for the -FOSS community to uphold traditional software freedoms and extend their normative commitments -into the emerging field of generative AI. - - This paper examines whether such an extension of copyleft principles is desirable and explores -potential mechanisms for achieving it. The Contextual Copyleft AI (CCAI) license is proposed, which may -cover both traditional software and generative AI contexts. Then, the challenges of evaluating licenses -that cross contextual boundaries are discussed, considering the legal and policy considerations in each -context. This leads to an argument about how such a license should be evaluated. Next, this structured -evaluation is completed for the CCAI license. The analysis concludes that the license could be -beneficial, particularly when it is used within a strong regulatory environment. The license provides -developers with greater control over how their code is used, encourages the development of open -source generative AI systems, and addresses concerns related to openwashing. Regulating the -responsible use of AI would address the risk-benefit imbalance inherent in copyleft, as highlighted by -the evaluation. This license highlights emerging cases where carefully constructed and thoughtfully - - 2 - evaluated copyleft licenses cross contextual boundaries, offering essential insights into how to preserve -and adapt free and open source norms, principles, and practices in a changing technological landscape. - -2. The Contextual Copyleft AI (CCAI) License -This section proposes a new open source license that harnesses the features of copyleft to enable -developers to allow only open source generative AI models to train on their code. The copyleft scheme -enables copyright holders to permit users of their software to use, modify, and redistribute their code -in any way, provided that the modified or redistributed version is licensed under the same terms as -the original work (Stallman 2002). Fundamentally, copyleft is a mechanism that leverages the existing -copyright system to protect and promote the freedom of users to run, copy, distribute, study, change, -and improve software. - - We will refer to the proposed license as the Contextual Copyleft AI (CCAI) license, a name that -draws on this license’s ability to cross technical and legal contexts. This ability necessitates a nuanced -evaluation of this license in the sections that follow. The full license text and a clause that can be added -to existing open source licenses are included in the appendix of this paper. The CCAI license consists -of three main components: - - 1. Software freedom: Users can run the software, modify the code to fit their needs, and - distribute copies of the software. - - 2. Copyleft requirement: Any verbatim copy, modified version, derivative work, or model trained - on this code must also be wholly licensed under CCAI with no additional restrictions. This - includes a description of the training data, the code used to train and run the model, and the - model’s parameters for generative AI models.1 - - 3. Distribution clarification: Any distributed CCAI software must include source code or provide - a way to access it. Providing access to software over the network is considered equivalent to - distributing the software. - -The CCAI license incorporates a copyleft, share-alike clause to propagate open source requirements -from training data to the AI model itself. It adheres to the open source norm of imposing terms on -distribution without using code to protect software freedom via an extended definition of “distribute.” -Furthermore, it enhances existing open source copyleft licenses by explicitly defining terms related to - -1 The description of the training data is inspired by the Open Source Initiative’s “Open Source AI” definition found at -https://opensource.org/ai/open-source-ai-definition. Description should generally include what data was used to train -the model, where the data originated, how it was selected and obtained, and discussion of any training or fliting methods. - - 3 - generative AI, such as including non-code aspects of an AI model in the definition of “entire work,” -which helps enable developers to freely share their code online while preventing the unconstrained -training of AI models with their work. This is based on the premise that using code to train generative -AI models is not considered fair use, as discussed above. Note that adding the CCAI clause to an -existing open-source license effectively creates a new variant of the license. Care should be taken to -ensure compatibility with other open-source licenses, as some existing licenses (such as the GPL) do -not permit additional restrictions beyond their stated terms. - - The first component of the license is software freedom, defined as allowing users to “run the -software, modify the code to fit their needs, and distribute copies of the software.” This establishes -the license’s goal and ensures that the usage rights of the code are granted to the software user -unconditionally, in accordance with all other open source licenses. This provision is essential for -making the license open source and supporting all four software freedoms. - - The second component of the license is the copyleft requirement, which mandates that “any -verbatim copy, modified version, derivative work, or model trained on this code must also be wholly -licensed under CCAI, with no additional restrictions.” This requirement closely resembles the copyleft -stipulations in the GNU General Public License (GPL) and Affero General Public License (AGPL) -licenses,2 with the added categorization of AI models as part of derivative works. This definition would -impose share-alike terms on AI models if using code for training is not deemed fair use (if such training -were fair use, the original copyright holder’s license conditions could not apply to the model). -However, while the courts have yet to determine whether this training constitutes fair use, it is crucial -to include this clarification. The most significant addition to the fundamentals of the GPL and AGPL -is the second part of this component, which outlines the specific elements that must be licensed under -CCAI, particularly for AI models. The open sourcing must encompass “a description of the training -data, the code used to train and run the model, and the model’s parameters” under the same license -without additional restrictions. This definition was based on the Open Source Initiative’s definition of -Open Source AI.3 This definition helps combat open-washing, because it specifies the minimum -threshold for what counts as an open source model in the eyes of a copyleft license. - - The third component of the license is the distribution clarification, a clause taken from the -AGPL license that categorizes accessing software over the network as distributing the software being -accessed. This clarification is essential to ensure that model users’ access via the internet is considered - -2 These are two popular OSS licenses, with AGPL targeted towards network-facing software. -3 https://opensource.org/ai/open-source-ai-definition - - 4 - “distributed” rather than being used by the developer. Otherwise, a model trained on data licensed -under CCAI could allow users to access it through a website without requiring the model to be open -source, as these obligations come into effect upon distribution. - -3. Evaluating Licenses Propagating Between Contexts -Licenses that are designed to propagate between works in different technical and legal contexts pose -unique challenges in evaluating their legal and policy implications. This section argues that the -changing context of such licenses necessitates three components. First, a legal analysis is conducted -to determine whether the propagation mechanism is permissible within the existing legal framework. -Second, a policy analysis is performed to assess the individual benefits of copyleft within each context. -This analysis must then be synthesized to determine the overall desirability of such licenses. While the -necessity of the legal analysis is straightforward, the policy consideration is more nuanced. An open -source license may be desirable in its original context and be able to propagate to another context, but -the license may not be desirable in that new context. There are additional considerations, as the -primary justification for copyleft licenses is necessarily context-dependent; therefore, the rationale for -a license will change as it propagates between contexts. After introducing this framework for -evaluating licenses that propagate across different technical and legal contexts, it will be applied to the -CCAI license proposed above. - - Technical contexts refer to sets of software that encompass work with similar risks of being -made open source. For instance, traditional software refers to software that is commonly open sourced -today, including desktop applications, computer science tools, and server software. The risks -associated with traditional open source software may include security vulnerabilities, dependency -management challenges, and concerns regarding project sustainability (Zajdel, Costa, and Mili, 2022). -Another context could be generative AI because the risks of releasing a generative AI model as open -source would be fundamentally different; the risks are focused on the harm that could be caused either -by humans using the model or by a misaligned model itself (Amodei et al. 2016). These technical -contexts may or may not exist within the same legal contexts. Typically, technologies operating in -various technical contexts are governed by the same overarching legal framework, although specific -regulatory requirements may differ. - - The first component in evaluating a license designed to propagate between works in different -contexts is a legal evaluation of the feasibility of the propagation mechanism. While the necessity of -such analysis is self-evident, the examination itself may prove complex. This evaluation must primarily - - 5 - establish two elements: first, that the work to which the license propagates qualifies as a derivative of -the original work under applicable copyright law; and second, that the incorporation of the original -work within the derivative work falls outside the scope of the fair use doctrine. If these conditions are -satisfied, it can generally be concluded that the license governing the original work may impose legal -obligations on its use within the derivative work, including the enforcement of share-alike provisions. - - The second component is a policy evaluation of the use of copyleft in each context -individually. This must be done in each context individually because different technical contexts -present fundamentally different risk profiles that affect the cost-benefit analysis underlying the -justification of free software. These varying risks mean that the same copyleft license may be justified -in one context but unjustified in another, even when applied to functionally related software. The -conclusions from the separate policy evaluations must be considered jointly in determining whether -such a license should be used. - - Copyleft licenses are politically justified by their ability to protect and promote free and open -source software.4 Thus, the justification for using copyleft within a specific context depends on the -rationale for using free and open source software in that context. If the use of free software generally -can’t be shown to be beneficial in a technical and legal context, then copyleft can’t be shown to be -beneficial either. Furthermore, the justification of free software must also consider the risks associated -with making a piece of software open source. Thus, when technical contexts change, the specifics of -the justification for free software, and therefore the justification for using copyleft, must change as -well. The overarching argument in support of free software may remain unchanged; however, the -specifics of the argument must evolve either way. For instance, copyleft on a traditional piece of -software may be justified by a pragmatic argument regarding the quality of software produced by free -and open source communities. If this license propagates to a generative AI system, the justification -may or may not change, remaining pragmatic or taking on moral terms instead. However, the specifics -of the argument must change due to the different fundamental risks associated with making the -software open source. Therefore, the policy evaluation must be conducted individually for each -technical context. - - The third component is a policy analysis of copyleft propagation as a whole. While the policy -analysis justifying the use of copyleft and open source must be conducted individually in each context, -the results should be considered collectively to make a final determination as to whether the license - -4 https://www.gnu.org/licenses/licenses.html - - 6 - should be used or not. This synthesis must evaluate the specific benefits and drawbacks of the -propagation mechanism itself, including: whether it empowers developers by giving them appropriate -control over how their work is used across contexts; whether it creates positive incentives for open -source development in the new context; whether it helps maintain the integrity of open source -principles as they extend to new technologies; and whether these benefits of cross-context propagation -outweigh any tensions created by applying the same license across different risk profiles. The synthesis -should conclude with a holistic assessment of whether the propagation serves the overarching goals -of the FOSS community. - -4. Contextual Copyleft from Traditional Software to Generative AI -In this section, we evaluate the proposed CCIA license using the three-part analysis described in the -previous section. First, we examine the legal mechanisms through which copyleft licenses could extend -from training data to AI models, focusing on the critical question of whether AI training constitutes -fair use under US copyright law. This legal analysis proceeds under the assumption that such training -does not automatically qualify as fair use, allowing us to explore how licensing terms might apply. - - Second, we evaluate the use of copyleft licenses in each context individually—traditional -software and generative AI—to understand how the justification for these licenses changes across -contexts. This comparative analysis reveals significant differences in the risk profiles between -traditional open source software and open source AI, particularly regarding the potential for direct -misuse and harm generation. - - Finally, we synthesize these analyses to demonstrate that a copyleft license that propagates to -AI systems offers three key benefits: it grants developers greater agency over how their code is used -in AI development, stimulates the creation of open source generative AI systems, and combats “open- -washing” practices. Practical enforcement of the CCAI license against AI models trained on publicly -available data might be challenging due to difficulties in detecting unauthorized usage. However, even -in cases where enforcement is challenging, the existence of clearly articulated license terms can serve -as an effective deterrent against non-compliant use by law-abiding entities. We also acknowledge that -the increased risks associated with open source AI, particularly the potential for direct misuse, require -consideration of complementary regulatory approaches. We conclude by arguing that responsible AI -regulation can work alongside copyleft licensing to better align the risks and benefits of free software -principles in the context of generative AI. - - 7 - 4.1. Legal Analysis -Consider the case of a developer trying to prevent their publicly published work from being used to -train generative AI models. What legal tools are available to this person, and under what circumstances -can they be used? - - Under United States (US) copyright law, authors of original works receive several exclusive -rights, codified in 17 U.S.C. § 106. These include the rights to reproduce the work, prepare derivative -works based on it, distribute copies to the public, and publicly perform or display the work. However, -these rights are subject to statutory limitations, including exceptions such as fair use and compulsory -licensing schemes that serve broader social and economic interests. Copyright automatically applies to -original works when they are fixed in a tangible medium, including digital formats such as online -content. This means that any original code published online is immediately protected. Licenses specify -the rights that are legally reserved for the copyright holder and grant other rights to users of the work, -often with attached terms and conditions. In the case of copyleft, this arrangement is reversed via a -license, with all rights granted to the user on the condition that derivative works are released under -the same license. - - Fair use is the primary limitation of licenses serving as a method to grant developers greater -control over how their code is used. Licenses, as contracts between the copyright owner and the -software user, can only place terms on rights reserved for the copyright holder. Under US copyright -law, the doctrine of fair use, determined by a set of four standards, allows an existing work to be used -to create a new work such that it is no longer considered a derivative work of the original if the new -work is sufficiently “transformative.” Therefore, the original copyright holder cannot impose -conditions on how their work is used if the new work qualifies as fair use. - - Regarding generative AI, if it becomes established through the courts or legislation that using -code to train a model is always fair use of that training data, the discussion of licenses in this context -becomes irrelevant. Licenses would be unable to restrict what models can train on, given that publicly -available code is unrestricted. However, there is uncertainty about whether training models on code -is, or should be, regarded as fair use (Charlesworth 2024). For instance, some argue that the -substantially similar relationship between the input and output of a model, the commercial purpose -of many AI, models, and how models can replace the source material in the market suggest that -training on copyrighted material may not qualify as fair use (Shen 2024). - - The May 2025 US Copyright Office report provides crucial (although non-binding) guidance -on this uncertainty. The report concludes that training AI models on copyrighted works “clearly - - 8 - implicates the right of reproduction,” explicitly rejecting any blanket fair use protection for AI training -(US Copyright Office 2025). The Office emphasized that the reproduction right is triggered when -copyrighted works are copied into training datasets, regardless of whether those copies are retained -after the training process is complete. This finding directly challenges the argument that AI training -constitutes non-expressive use, exempt from copyright concerns. The report further notes that -commercial uses creating competing content are less likely to qualify as fair use, while research and -non-commercial uses receive more favorable treatment—a distinction particularly relevant for -commercial code generation models. - - With the assumption that this sort of training is not fair use, we can hypothesize that -developers can use licenses to restrict how their code is used to train generative AI models. In this -case, generative AI models trained on code could plausibly be considered derivative works of the -training data, although this interpretation remains legally unsettled, as the exact code from the training -data is not present in the model’s codebase and is never technically executed by the model. A similar -conclusion as to the ability of open source licenses to propagate to AI models has been found where -Benhamou argues “when the AI model incorporates open code into its program or open data into its -training dataset, there is a risk of propagation, i.e. a risk that the entire model or the input data become -fully open, as the AI model or the input can be considered as derivative of the open code or data, -whose license requires derivatives to remain open” (Benhamou 2024). - - The uncertainty surrounding whether the use of open source code as training data constitutes -fair use warrants consideration in its own right. The current confusion in the legal landscape naturally -leads developers to restrict licenses defensively, potentially hindering both AI innovation and open -source collaboration. A clear, purpose-built license can overcome this confusion by providing explicit -guidance that operates effectively under multiple legal scenarios. If training is not deemed fair use, -then licenses become the primary mechanism for controlling the use of AI training. A license -specifically addressing AI training for code generation models would provide clear guidance to all -parties, potentially increasing the pool of code available for training while respecting developer -preferences. However, if courts ultimately determine that training AI models constitutes fair use, such -licensing terms would have no legal force. And it is worth noting that jurisdictions outside the US, -such as the EU, provide explicit text and data mining (TDM) exceptions, which permit data mining -unless right-holders explicitly opt-out through licensing terms. Thus, the CCAI license terms could -effectively serve as an explicit opt-out of such statutory exceptions under EU law. Therefore, we see - - 9 - legal value in such a license, regardless of how the US courts ultimately rule on the question of fair -use. - -4.2. Policy Analysis in Each Context -The FOSS community generally agrees on the benefits of FOSS, which, according to the community, -include reliability, security, and low cost (AlMarzouq et al. 2005). However, sub-movements of the -community disagree on the intellectual justification for FOSS. The free software (FS) movement -presents moral claims to advocate for free software as defined above. Then, there is the open source -(OS) movement, which offers pragmatic arguments regarding the value of free software. The -differences can be summed up as “open source is a development methodology; free software is a -social movement,” but their “practical recommendations” are largely similar (Stallman 2002). - - The FS movement argues for free software based on what Richard Stallman describes as the -“Golden Rule” applied to software development. He argues that proprietary software is destructive -insofar as it “reduces the amount and the ways that a program can be used” and, as a result, reduces -the wealth humanity derives from the program (Stallman 2002). He ties this to Kantian ethics, arguing -that a good citizen does not use destructive means, such as proprietary software, to become wealthier. -With this, he concludes that “programmers have the duty to encourage others to share, redistribute, -study, and improve the software we write” (Stallman 2002). Another way that this has been formulated -is to start from the premise that software has permeated society, and, under a proprietary model, -software producers can control the availability of and access to software. As a result, “we are beholden -to these producers to run our modern society and are theoretically at their whim” (Heyns 2012). -Proponents of FS argue that this outcome is undesirable. Instead, software should be regarded as -infrastructure, as it is essential for societal participation and engagement. Thus, the assertion is made -that society should regulate access to the software “infrastructure” rather than software companies. -This approach helps prevent what has been termed “the digital divide,” wherein select groups of -people have access to the broad digital ecosystem (James 2003). - - The OS movement advocates for free software from a strictly pragmatic or utilitarian -framework, arguing that free software is more beneficial to society than proprietary software. Software -plays a critical role today, making society interested in the quality and reliability of the software that is -produced and used. OS advocates argue that making source code freely available online for public -collaboration results in high-quality and highly reliable software. The OS community generally posits -that this is due to the ability to peer review software and provide more timely fixes because more - - 10 - developers can review source code in search of bugs and security risks. When these issues are -identified, a distributed community can work together to create solutions, rather than a select group -within a specific company addressing the problem. The idea that a distributed community can lead to -better software outcomes is summed up in the famous Linus’s Law: “Given enough eyeballs, all bugs -are shallow” (Raymond 1999). OS is distinct from FS in that it does not make moral claims, but is -based on pragmatic claims regarding which development model will yield the highest quality and most -reliable software. - - The primary risks of releasing certain pieces of traditional software as open source include -security vulnerabilities and software misuse (Zajdel, Costa, and Mili 2022). Regarding security -vulnerabilities, the risk of malicious actors discovering and exploiting these vulnerabilities can be -mitigated by allowing multiple developers to review the code and identify and fix bugs. Thus, “open -source does not pose any significant barriers to security but rather reinforces sound security practices -by involving many people that expose bugs quickly and offers side-effects that provide customers and -the community with concrete examples of reusable, secure, and working code” (Clarke, Dorwin, and -Nash 2009). - - Moving from the traditional software context to generative AI, the risk profile changes. The -security risks of open source AI are non-negligible, with a survey revealing that 10% of IT decision- -makers have accidentally installed malicious code in their organizations through the use of open source -AI tools.5 Furthermore, the risk of actors using the code for malicious purposes is a concern that is -less directly countered by the benefits of open source software and is more challenging to mitigate. -Once open source software is out in the world, there is by definition no way that the developers can -control its use; while Meta’s Llama terms of service ostensibly prohibit its use for military purposes, -researchers associated with the Chinese People’s Liberation Army were still able to use it to create a -tool to gather and synthesize intelligence (Wilson and Hine 2025). - - Additional risks associated with open source AI include misuse, where software is used in a -technically unintended manner to inflict harm. When it comes to OSS related to generative AI, there -are several pathways to explicit harm, including the exploitation of security vulnerabilities and the -ability to create harmful content, particularly given the challenges of rolling back or updating models -(Eiras, Petrov, Vidgen, Witt, et al. 2024). The possibility of generating harmful content or using a -foundation model for malicious activities is particularly concerning given the nature of such misuse. - -5 https://www.anaconda.com/lp/state-of-enterprise-open-source-ai - - 11 - This type of misuse is primarily direct, contrasting with actor-dependent misuse seen in traditional -software. For example, if a foundation model is fine-tuned on a company’s emails, it may generate -highly effective phishing emails, posing a more significant threat to unsuspecting employees. This -scenario differs fundamentally from using an open source email client to send phishing emails. Here, -the open source technology in the case of generative AI is being used to amplify harm. In contrast, in -the traditional software case, the open source software operates as intended but is misused by the -actor to inflict damage. The risk of this explicit misuse in generative AI is significantly higher compared -to traditional software. - -4.3 Synthesizing the Analysis -The legal analysis has shown that the CCAI license can propagate from training data to a generative -AI model itself, while the policy analysis has shown strong support for the open-sourcing of traditional -software, highlighting both substantial benefits and risks in the context of open source AI. The -particular risk of direct misuse stands out as a significant concern, particularly in the context of open -source AI, compared to traditional software, which could complicate the justification for open source -approaches in the generative AI context. In this section, we will take these insights and show that the -CCAI license could benefit both the open source and AI development communities when used in -particular regulatory schemes. The promise such a license holds for both communities is first argued, -and then a discussion of regulatory schemes follows. - - This type of copyleft license offers three key advantages. First, it would give developers -enhanced control over how their code is incorporated into generative AI development. Second, it -would promote the development of genuinely open source generative AI systems. Third, it would help -combat “open-washing” practices that dilute the real benefits that open source generative AI systems -can provide. - - First, a copyleft license that propagates from traditional software to generative AI systems -provides an important mechanism to give developers greater control over how their code is used in -AI systems. Today, when developers open source their code, they lose control over what types of -generative AI models are trained on their code and for what purposes those AI models are employed. -On the one hand, this is a feature, not a bug. Open source licenses are explicitly designed to allow the -unrestricted use of code, enabling others to use the original work in new and innovative ways. At the -same time, developers often do not want their work to contribute to nefarious ends, which could -occur if their code is used to train a generative AI model intended to cause harm, for example. This - - 12 - has led some developers to add “no AI” clauses to existing licenses to prohibit using their code for -training an AI model.6 - - The tension within the open source community regarding the balance between freely sharing -software and caution towards unrestricted training on such software is evident in the backlash against -the release of CoPilot. GitHub CoPilot is an AI model developed specifically to generate code and, -consequently, requires a vast amount of code for its training data. This prompted GitHub to use code -from numerous open source repositories, asserting that the training of their model constituted fair use -and, therefore, the licensing terms of the code did not apply. Many in the FOSS community disagreed, -leading to a boycott of GitHub by many open source developers and a class-action lawsuit against -GitHub, Microsoft, OpenAI, and others involved in creating CoPilot (Class Action Complaint 3:22- -cv-06823). Some even argue that foundational models like CoPilot may discourage developers from -contributing to open source projects, as these models “exploit the principle of solidarity operating -within the movement” and have the potential to undermine the movement altogether (Novobilská -2023). An open source license with a copyleft clause would specify that it propagates from training -data to generative AI models, providing open source developers a middle ground between allowing -unconstrained training and limiting innovation and openness by restricting all training. - - Second, introducing and adopting a copyleft license that propagates from training data to AI -models would lead to more open source generative AI models—an outcome that the open source -community supports.7 It is well known that training generative AI models requires vast amounts of -data, so a model designed to generate code (such as GitHub’s CoPilot) would need substantial code -to train on. Open source repositories are currently the solution for model developers. Suppose such a -license were widely adopted within the FOSS community. Of course, the effectiveness of the CCAI -license depends heavily on its widespread adoption. If only a small subset of developers adopts it, -proprietary AI developers may simply avoid CCAI-licensed code. A license of this kind would outline -which portion of the open source code online is available for use by open source models during -training. This would create an incentive for the open sourcing of new generative AI models that -produce code, as it provides open source model developers with access to training data that proprietary -models cannot use. The incentivization and creation of open source generative AI models offer new -opportunities for external oversight, technical innovation, and decentralized control over AI -development compared to closed-source models (Seger et al. 2023). - -6 https://github.com/non-ai-licenses/non-ai-licenses -7 https://opensource.org/ai/open-source-ai-definition - - 13 - Third, a copyleft license of this type could provide more explicit, enforceable definitions for -open source AI, helping to combat “open-washing.” Open-washing refers to AI models that claim to -be open source by publicly sharing only one component, such as the model weights (Liesenfeld and -Dingemanse 2024). While this may allow users to download and run the model on their computers, it -fails to convey the comprehensive understanding of software freedom that the open source -community has cultivated; crucial contexts, including the code that trained the model and details of -the training data, are absent. The current language in copyleft licenses mandates that the “entire work” -be shared under the same license as the original, not just the portion of the software copied from the -original work. From a model developer’s perspective, the “entire work” could encompass not only the -model weights but also the underlying model architecture, supporting code, and details about the -training data. The interpretation of “entire work” as it applies to generative AI could broaden the -required components of an AI system that must be open to tackle the open-washing observed in the -AI development community effectively. - - These three advantages of the CCAI license must be considered in light of the above legal and -policy analysis. Specifically, the risk of direct misuse of open source AI is that it is used for malicious -purposes. The challenge is that open source licenses must not restrict how a technology can be used, -as defined by the principles of open source.8 However, while it can’t be prohibited, there is precedent -within the FOSS community resisting the misuse of open source software. For instance, a former -developer at Chef, a computer management software company, created open source tools that helped -customers work with Chef’s software. After leaving the company, he discovered a contract between -Chef and the US Immigration and Customs Enforcement (ICE), prompting him to remove the code -with a note stating, “I have a moral and ethical obligation to prevent my source [code] from being -used for evil.”9 This decision caused an outage for multiple customers, leading Chef to republish an -older version of the open source tool’s source code on the company’s corporate GitHub account, an -action permitted by the software freedom provisions of the original license. However, it should be -noted that the developer was able to protest Chef’s collaboration with ICE because he had authority -over that code. - - One possible way to mitigate the risks of open source generative AI would be to introduce a -responsible AI use regulation that limits how open source models can be used to cause direct harm. -For instance, part of the EU AI Act prevents AI systems from using subliminal, manipulative, or - -8 https://opensource.org/osd -9 https://www.zdnet.com/article/no-source-code-for-evil-developers-pressure-chef-software-to-cut-ties-with-ice/ - - 14 - deceptive techniques that distort a person’s behavior and impair decision-making, causing significant -harm, or exploiting individuals or groups based on age, disability, or socio-economic conditions to -influence behaviors in a harmful way (European Union 2024). Although these provisions have been -criticized for a lack of specificity (Montag and Finck 2024), similar restrictions in the US could help -mitigate the risk of misuse of open source generative AI systems. This would help rebalance the risks -and benefits of open source generative AI closer to those of traditional open source software, allowing -for a stronger endorsement of copyleft in both contexts. - -5. Conclusion -The analysis presented in this article demonstrates that the Contextual Copyleft AI (CCAI) license -represents a promising approach to extending traditional copyleft principles into the domain of -generative AI development. Through our structured evaluation framework, we have determined that -such a license is legally feasible under current copyright law, assuming that AI training does not -constitute fair use, and offers substantial benefits to both the open source and AI development -communities. The license addresses three critical challenges facing the FOSS community in the age of -generative AI: providing developers with meaningful control over how their code is incorporated into -AI systems, creating incentives for genuinely open source AI development, and combating the dilution -of open source principles through open-washing practices. These advantages align with the -fundamental goals of the FOSS movement while adapting to the unique characteristics of generative -AI technologies. - - However, our policy analysis reveals that the risk profile of open source generative AI differs -significantly from that of traditional software, particularly regarding the potential for direct misuse, -where AI systems can be employed to generate harmful content or amplify malicious activities. This -increased risk profile challenges the traditional cost-benefit calculus that has historically justified -copyleft licensing in software development. The analysis suggests that while the benefits of open -source AI remain substantial—including enhanced oversight, innovation, and decentralized control— -the heightened risks require careful consideration and potentially complementary regulatory -mechanisms to achieve an appropriate balance. - - The path forward for implementing the CCAI license depends critically on the development -of comprehensive regulatory frameworks that address the responsible use of AI systems. Such -regulations, exemplified by provisions in the EU AI Act that restrict manipulative and exploitative AI -applications, could help mitigate the direct misuse risks that complicate the justification for open - - 15 - source AI. CCAI alone does not eliminate misuse risks—it ensures openness but cannot prevent -malicious use of openly released models. Thus, its practical effectiveness depends significantly on -complementary regulatory measures addressing responsible use of AI. Within such a regulatory -environment, the CCAI license emerges as a valuable tool for preserving the core values of software -freedom while adapting to the realities of modern AI development. This analysis thus contributes to -the broader discourse on maintaining the integrity and relevance of FOSS principles as technology -continues to evolve, demonstrating that thoughtful adaptation of traditional licensing mechanisms can -help navigate the complex challenges posed by generative AI while upholding the fundamental -commitments of the open source community. - - 16 - Appendix A: The CCAI License -The following is the text of the CCAI license designed to propagate from training data to generative -AI models. - - Contextual Copyleft AI (CCAI) License v1.0 - - Copyright (c) [year] [author] - - Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated - documentation files (the “Software”), to use, copy, modify, merge, publish, distribute, and/or sublicense copies - of the Software, subject to the following conditions: - - 1. Software Freedom. - Users are granted the freedom to run, study, modify, and distribute the Software, including modified versions, - for any purpose. - - 2. Copyleft Requirement. - Any distribution, publication, or making available of the Software — including exact copies, modified versions, - or derivative works — must be licensed in full under the terms of this CCAI License without imposing any - further restrictions. This requirement also applies to any model, dataset, or system that is trained on, derived - from, or incorporates the Software or its outputs. In the case of generative AI models, the obligation to apply - this license includes, at minimum, the disclosure of the model’s source code, a meaningful description of the - training data, and the model’s parameters, weights, and architecture. - - 3. Distribution Clarification. - Providing access to the Software or any covered model over a network or online service shall be considered - distribution and triggers the obligations of this License. - - 4. No Warranty. - The Software is provided “as is”, without warranty of any kind. - - 5. Preservation of License Notice. - Copies, modifications, and derivative works must retain this license notice. - -Appendix B: The CCAI Clause -The following is the text of the CCAI clause that can be added to existing open source licenses to -propagate the license from training data to generative AI models. - - Contextual Copyleft AI (CCAI) Clause - - If this software is used to train, fine-tune, or otherwise generate any machine learning model or generative AI - system, any resulting model, dataset, or system must be released under terms no more restrictive than this license. - This includes the requirement to make available the training code, a description of the data used in training, and - the model’s parameters and architecture. Providing access to such a model or its outputs via a network is - considered distribution and triggers this obligation. - - 17 - References - -“1-0-Github_complaint.Pdf.” n.d. Accessed February 19, 2025. - https://githubcopilotlitigation.com/pdf/07074/1-0-github_complaint.pdf. - -AlMarzouq, Mohammad, Li Zheng, Guang Rong, and Varun Grover. 2005. “Open Source: - Concepts, Benefits, and Challenges.” Communications of the Association for Information Systems 16. - https://doi.org/10.17705/1CAIS.01637. - -Amodei, Dario, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané. - 2016. “Concrete Problems in AI Safety.” arXiv. - https://doi.org/10.48550/arXiv.1606.06565. - -Bandi, Ajay, Pydi Venkata Satya Ramesh Adapa, and Yudu Eswar Vinay Pratap Kumar Kuchi. 2023. - “The Power of Generative AI: A Review of Requirements, Models, Input–Output Formats, - Evaluation Metrics, and Challenges.” Future Internet 15 (8): 260. - https://doi.org/10.3390/fi15080260. - -Benhamou, Yaniv. 2024. “Open Source AI: Does the Copyleft Clause Propagate to Proprietary AI - Models? Revisiting the Definition of Derivatives in the AI-Context.” SSRN Scholarly Paper. - Rochester, NY: Social Science Research Network. https://doi.org/10.2139/ssrn.4859623. - -Charlesworth, Jacqueline. 2024. “Generative AI’s Illusory Case for Fair Use.” SSRN Scholarly Paper. - Rochester, NY: Social Science Research Network. https://doi.org/10.2139/ssrn.4924997. - -Clarke, Russell, David Dorwin, and Rob Nash. 2009. “Is Open Source Software More Secure?” -Contractor, Danish, Daniel McDuff, Julia Katherine Haines, Jenny Lee, Christopher Hines, Brent - - Hecht, Nicholas Vincent, and Hanlin Li. 2022. “Behavioral Use Licensing for Responsible - AI.” In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, 778– - 88. FAccT ‘22. New York, NY, USA: Association for Computing Machinery. - https://doi.org/10.1145/3531146.3533143. -Eiras, Francisco, Aleksandar Petrov, Bertie Vidgen, Christian Schroeder, Fabio Pizzati, Katherine - Elkins, Supratik Mukhopadhyay, et al. 2024. “Risks and Opportunities of Open-Source - Generative AI.” arXiv. https://doi.org/10.48550/arXiv.2405.08597. -Eiras, Francisco, Aleksandar Petrov, Bertie Vidgen, Christian Schroeder de Witt, Fabio Pizzati, - Katherine Elkins, Supratik Mukhopadhyay, et al. 2024. “Near to Mid-Term Risks and - Opportunities of Open-Source Generative AI.” arXiv. - https://doi.org/10.48550/arXiv.2404.17047. -European Union. 2024. “Regulation - EU - 2024/1689 - EN - EUR-Lex.” 2024. https://eur- - lex.europa.eu/eli/reg/2024/1689/oj/eng. -Floridi, Luciano, Carlotta Buttaboni, Emmie Hine, Claudio Novelli, Tyler Schroder, and Grant - Shanklin. 2025. “Open-Source AI Made in the EU: Why It Is a Good Idea.” SSRN Scholarly - Paper. Rochester, NY: Social Science Research Network. - https://doi.org/10.2139/ssrn.5191372. -Gozalo-Brizuela, Roberto, and Eduardo C. Garrido-Merchan. 2023. “ChatGPT Is Not All You - Need. A State of the Art Review of Large Generative AI Models.” arXiv. - https://doi.org/10.48550/arXiv.2301.04655. -Heyns, Emiliano. 2012. “The Ethics of Free Software,” December. - https://www.academia.edu/2320407/The_Ethics_of_Free_Software. -James, Jeffrey. 2003. “Free Software and the Digital Divide: Opportunities and Constraints for - Developing Countries.” Journal of Information Science 29 (1): 25–33. - https://doi.org/10.1177/016555150302900103. -Liesenfeld, Andreas, and Mark Dingemanse. 2024. “Rethinking Open Source Generative AI: Open- - Washing and the EU AI Act.” In Proceedings of the 2024 ACM Conference on Fairness, - - 18 - Accountability, and Transparency, 1774–87. FAccT ‘24. New York, NY, USA: Association for - Computing Machinery. https://doi.org/10.1145/3630106.3659005. -Montag, Christian, and Michèle Finck. 2024. “Successful Implementation of the EU AI Act - Requires Interdisciplinary Efforts.” Nature Machine Intelligence 6 (12): 1415–17. - https://doi.org/10.1038/s42256-024-00954-z. -Novobilská, Linda. 2023. “Free and Open Source Software Licensing Requirements and Copyright - Infringement Involving Artificial Intelligence Technologies.” -Phillips, Ron. 2007. “Deadly Combinations: A Framework for Analyzing the GPL’s Viral Effect.” - John Marshall Journal of Computer and Information Law 25:487. -Raymond, Eric. 1999. “The Cathedral and the Bazaar.” Knowledge, Technology & Policy 12 (3): 23–49. - https://doi.org/10.1007/s12130-999-1026-0. -Seger, Elizabeth, Noemi Dreksler, Richard Moulange, Emily Dardaman, Jonas Schuett, K. Wei, - Christoph Winter, et al. 2023. “Open-Sourcing Highly Capable Foundation Models: An - Evaluation of Risks, Benefits, and Alternative Methods for Pursuing Open-Source - Objectives.” arXiv. https://doi.org/10.48550/arXiv.2311.09227. -Shen, Celeste. 2024. “Fair Use, Licensing, and Authors’ Rights in the Age of Generative AI.” - Northwestern Journal of Technology and Intellectual Property 22 (1): 157. -Stallman, Richard. 2002. Free Software, Free Society: Selected Essays of Richard M. Stallman. Lulu.com. -US Copyright Office. 2025. “Copyright and Artificial Intelligence, Part 3: Generative AI Training - Pre-Publication Version,” May. -Wilson, Claudia, and Emmie Hine. 2025. “US Open-Source AI Governance: Balancing Ideological - and Geopolitical Considerations with China Competition.” Center for AI Policy. - https://cdn.prod.website- - files.com/65af2088cac9fb1fb621091f/67aaca031ed677c879434284_Final_US%20Open- - Source%20AI%20Governance.pdf. -Zajdel, Stan, Diego Elias Costa, and Hafedh Mili. 2022. “Open Source Software: An Approach to - Controlling Usage and Risk in Application Ecosystems.” In Proceedings of the 26th ACM - International Systems and Software Product Line Conference - Volume A, A:154–63. SPLC ‘22. New - York, NY, USA: Association for Computing Machinery. - https://doi.org/10.1145/3546932.3547000. - - 19 -