commit d60628d0efa80372d54041a6b2914aebc1272b76 equwal <13551856+equwal@users.noreply.github.com> 2026-10-05 01:20:02 -0700 git mirror: regenerate (CCAI license files in public repos)
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diff --git a/git/9p-store/commit/3ddb90591db214c338ef789110a59d68333739c9.html b/git/9p-store/commit/3ddb90591db214c338ef789110a59d68333739c9.html new file mode 100644 index 0000000..70fbe38 --- /dev/null +++ b/git/9p-store/commit/3ddb90591db214c338ef789110a59d68333739c9.html @@ -0,0 +1,39 @@ +<!DOCTYPE html> +<html lang="en"><head><meta charset="UTF-8" /> +<meta name="viewport" content="width=device-width, initial-scale=1" /> +<title>9p-store 3ddb9059 - Recently Written</title> +<link rel="stylesheet" href="../../stagit.css" /> +</head><body> +<div id="top"><a href="../../../index.html">Recently Written</a> · <a href="../../index.html">git</a></div> +<h1>9p-store</h1><span class="desc">Small disk-backed 9P file server (diod) for a private network</span><p class="url">git clone https://github.com/equwal/9p-store</p><p><a href="../../9p-store/index.html">Log</a> | <a href="../../9p-store/files.html">Files</a> | <a href="../../9p-store/refs.html">Refs</a></p><hr/> +<div id="content"> +<pre>commit 3ddb90591db214c338ef789110a59d68333739c9 +Spenser Truex <truex@equwal.com> +2026-10-04 23:58:18 -0700 + +LICENSE: add the Contextual Copyleft AI (CCAI) clause, 2026-10-04 +</pre><pre> LICENSE | 13 ++++++++++++- + 1 file changed, 12 insertions(+), 1 deletion(-) +</pre><pre>diff --git a/LICENSE b/LICENSE +index 777c6b0..c883102 100644 +--- a/LICENSE ++++ b/LICENSE +<span class="h">@@ -18,4 +18,15 @@ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span> + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +<span class="d">-SOFTWARE.</span> +\ No newline at end of file +<span class="a">+SOFTWARE.</span> +<span class="a">+</span> +<span class="a">+Contextual Copyleft AI (CCAI) Clause</span> +<span class="a">+Added 2026-10-04.</span> +<span class="a">+If this software is used to train, fine-tune, or otherwise generate any machine learning model or generative AI</span> +<span class="a">+system, any resulting model, dataset, or system must be released under terms no more restrictive than this license.</span> +<span class="a">+This includes the requirement to make available the training code, a description of the data used in training, and</span> +<span class="a">+the model’s parameters and architecture. Providing access to such a model or its outputs via a network is</span> +<span class="a">+considered distribution and triggers this obligation.</span> +<span class="a">+</span> +<span class="a">+Paper: https://arxiv.org/pdf/2507.12713</span> +<span class="a">+Mirror: https://dickt.store/fuckai.pdf</span></pre> +</div></body></html> diff --git a/git/9p-store/file/LICENSE.html b/git/9p-store/file/LICENSE.html index 18fca58..a49ab38 100644 --- a/git/9p-store/file/LICENSE.html +++ b/git/9p-store/file/LICENSE.html @@ -7,7 +7,7 @@ <div id="top"><a href="../../../index.html">Recently Written</a> · <a href="../../index.html">git</a></div> <h1>9p-store</h1><span class="desc">Small disk-backed 9P file server (diod) for a private network</span><p class="url">git clone https://github.com/equwal/9p-store</p><p><a href="../../9p-store/index.html">Log</a> | <a href="../../9p-store/files.html">Files</a> | <a href="../../9p-store/refs.html">Refs</a></p><hr/> <div id="content"> -<p>LICENSE (1062 bytes)</p><pre class="src"><a id="l1" href="#l1">1</a> MIT License +<p>LICENSE (1704 bytes)</p><pre class="src"><a id="l1" href="#l1">1</a> MIT License <a id="l2" href="#l2">2</a> <a id="l3" href="#l3">3</a> Copyright (c) 2026 equwal <a id="l4" href="#l4">4</a> @@ -27,5 +27,16 @@ <a id="l18" href="#l18">18</a> AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER <a id="l19" href="#l19">19</a> LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, <a id="l20" href="#l20">20</a> OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -<a id="l21" href="#l21">21</a> SOFTWARE.</pre> +<a id="l21" href="#l21">21</a> SOFTWARE. +<a id="l22" href="#l22">22</a> +<a id="l23" href="#l23">23</a> Contextual Copyleft AI (CCAI) Clause +<a id="l24" href="#l24">24</a> Added 2026-10-04. +<a id="l25" href="#l25">25</a> If this software is used to train, fine-tune, or otherwise generate any machine learning model or generative AI +<a id="l26" href="#l26">26</a> system, any resulting model, dataset, or system must be released under terms no more restrictive than this license. +<a id="l27" href="#l27">27</a> This includes the requirement to make available the training code, a description of the data used in training, and +<a id="l28" href="#l28">28</a> the model’s parameters and architecture. Providing access to such a model or its outputs via a network is +<a id="l29" href="#l29">29</a> considered distribution and triggers this obligation. +<a id="l30" href="#l30">30</a> +<a id="l31" href="#l31">31</a> Paper: https://arxiv.org/pdf/2507.12713 +<a id="l32" href="#l32">32</a> Mirror: https://dickt.store/fuckai.pdf</pre> </div></body></html> diff --git a/git/9p-store/files.html b/git/9p-store/files.html index 17c0418..7519113 100644 --- a/git/9p-store/files.html +++ b/git/9p-store/files.html @@ -7,5 +7,5 @@ <div id="top"><a href="../../index.html">Recently Written</a> · <a href="../index.html">git</a></div> <h1>9p-store</h1><span class="desc">Small disk-backed 9P file server (diod) for a private network</span><p class="url">git clone https://github.com/equwal/9p-store</p><p><a href="../9p-store/index.html">Log</a> | <a href="../9p-store/files.html">Files</a> | <a href="../9p-store/refs.html">Refs</a></p><hr/> <div id="content"> -<table><thead><tr><td>Mode</td><td>Name</td><td>Size</td></tr></thead><tbody><tr><td>100644</td><td><a href="file/9pstore.service.in.html">9pstore.service.in</a></td><td class="num">524</td></tr><tr><td>100644</td><td><a href="file/LICENSE.html">LICENSE</a></td><td class="num">1062</td></tr><tr><td>100644</td><td><a href="file/docs/USAGE.md.html">docs/USAGE.md</a></td><td class="num">2909</td></tr><tr><td>100755</td><td><a href="file/install.sh.html">install.sh</a></td><td class="num">1179</td></tr></tbody></table> +<table><thead><tr><td>Mode</td><td>Name</td><td>Size</td></tr></thead><tbody><tr><td>100644</td><td><a href="file/9pstore.service.in.html">9pstore.service.in</a></td><td class="num">524</td></tr><tr><td>100644</td><td><a href="file/LICENSE.html">LICENSE</a></td><td class="num">1704</td></tr><tr><td>100644</td><td><a href="file/docs/USAGE.md.html">docs/USAGE.md</a></td><td class="num">2909</td></tr><tr><td>100755</td><td><a href="file/install.sh.html">install.sh</a></td><td class="num">1179</td></tr></tbody></table> </div></body></html> diff --git a/git/9p-store/index.html b/git/9p-store/index.html index 9edcea4..5320ed4 100644 --- a/git/9p-store/index.html +++ b/git/9p-store/index.html @@ -7,5 +7,5 @@ <div id="top"><a href="../../index.html">Recently Written</a> · <a href="../index.html">git</a></div> <h1>9p-store</h1><span class="desc">Small disk-backed 9P file server (diod) for a private network</span><p class="url">git clone https://github.com/equwal/9p-store</p><p><a href="../9p-store/index.html">Log</a> | <a href="../9p-store/files.html">Files</a> | <a href="../9p-store/refs.html">Refs</a></p><hr/> <div id="content"> -<table><thead><tr><td>Date</td><td>Commit message</td><td>Author</td></tr></thead><tbody><tr><td>2026-10-04 03:38</td><td><a href="commit/660e4e95664729e1f4c3814e146063ab02edff9e.html">docs: client behind a WireGuard sidecar container</a></td><td>equwal</td></tr><tr><td>2026-10-04 03:27</td><td><a href="commit/e3b11be0cb5f58ffdf99a3a3fbcf9c4991a45d40.html">Initial version</a></td><td>equwal</td></tr></tbody></table> +<table><thead><tr><td>Date</td><td>Commit message</td><td>Author</td></tr></thead><tbody><tr><td>2026-10-04 23:58</td><td><a href="commit/3ddb90591db214c338ef789110a59d68333739c9.html">LICENSE: add the Contextual Copyleft AI (CCAI) clause, 2026-10-04</a></td><td>Spenser Truex</td></tr><tr><td>2026-10-04 03:38</td><td><a href="commit/660e4e95664729e1f4c3814e146063ab02edff9e.html">docs: client behind a WireGuard sidecar container</a></td><td>equwal</td></tr><tr><td>2026-10-04 03:27</td><td><a href="commit/e3b11be0cb5f58ffdf99a3a3fbcf9c4991a45d40.html">Initial version</a></td><td>equwal</td></tr></tbody></table> </div></body></html> diff --git a/git/9p-store/refs.html b/git/9p-store/refs.html index 56a461e..3698605 100644 --- a/git/9p-store/refs.html +++ b/git/9p-store/refs.html @@ -7,5 +7,5 @@ <div id="top"><a href="../../index.html">Recently Written</a> · <a href="../index.html">git</a></div> <h1>9p-store</h1><span class="desc">Small disk-backed 9P file server (diod) for a private network</span><p class="url">git clone https://github.com/equwal/9p-store</p><p><a href="../9p-store/index.html">Log</a> | <a href="../9p-store/files.html">Files</a> | <a href="../9p-store/refs.html">Refs</a></p><hr/> <div id="content"> -<table><thead><tr><td>Name</td><td>Type</td><td>Date</td><td>Commit</td></tr></thead><tbody><tr><td>main</td><td>commit</td><td>2026-10-04</td><td>660e4e9</td></tr></tbody></table> +<table><thead><tr><td>Name</td><td>Type</td><td>Date</td><td>Commit</td></tr></thead><tbody><tr><td>main</td><td>commit</td><td>2026-10-04</td><td>3ddb905</td></tr></tbody></table> </div></body></html> diff --git a/git/CSV/commit/0057099fbcfae89c4e718cac9225ce828fa221de.html b/git/CSV/commit/0057099fbcfae89c4e718cac9225ce828fa221de.html new file mode 100644 index 0000000..2ad5b24 --- /dev/null +++ b/git/CSV/commit/0057099fbcfae89c4e718cac9225ce828fa221de.html @@ -0,0 +1,735 @@ +<!DOCTYPE html> +<html lang="en"><head><meta charset="UTF-8" /> +<meta name="viewport" content="width=device-width, initial-scale=1" /> +<title>CSV 0057099f - Recently Written</title> +<link rel="stylesheet" href="../../stagit.css" /> +</head><body> +<div id="top"><a href="../../../index.html">Recently Written</a> · <a href="../../index.html">git</a></div> +<h1>CSV</h1><span class="desc">Convert CSV (in a reader macro, or in a file) in to Lisp lists.</span><p class="url">git clone https://github.com/equwal/CSV</p><p><a href="../../CSV/index.html">Log</a> | <a href="../../CSV/files.html">Files</a> | <a href="../../CSV/refs.html">Refs</a></p><hr/> +<div id="content"> +<pre>commit 0057099fbcfae89c4e718cac9225ce828fa221de +Spenser Truex <truex@equwal.com> +2026-10-05 00:02:49 -0700 + +Add ccai-2507.12713.pdf: the CCAI paper, arXiv:2507.12713 (CC BY-NC-SA 4.0) +</pre><pre> ccai-2507.12713.pdf | Bin 0 -> 327583 bytes + 1 file changed, 0 insertions(+), 0 deletions(-) +</pre><pre>diff --git a/ccai-2507.12713.pdf b/ccai-2507.12713.pdf +new file mode 100644 +index 0000000..7d50d46 +--- /dev/null ++++ b/ccai-2507.12713.pdf +<span class="h">@@ -0,0 +1,693 @@</span> +<span class="a">+The Case for Contextual Copyleft: Licensing Open Source Training Data and Generative AI</span> +<span class="a">+</span> +<span class="a">+Grant Shanklin1,2, Emmie Hine1,3,4, Claudio Novelli1, Tyler Schroder1,2, Luciano Floridi1,3</span> +<span class="a">+</span> +<span class="a">+1 Digital Ethics Center, Yale University, 85 Trumbull St, New Haven, CT 06511</span> +<span class="a">+2 Department of Computer Science, Yale University, 51 Prospect St, New Haven, CT 06511</span> +<span class="a">+3 Department of Legal Studies, University of Bologna, Via Zamboni 27/29, 40126 Bologna, Italy</span> +<span class="a">+4 Center for IT & IP Law, KU Leuven, Sint-Michielsstraat, 6 30000 Leuven, BE</span> +<span class="a">+</span> +<span class="a">+Email for correspondence: grant.shanklin@yale.edu</span> +<span class="a">+</span> +<span class="a">+Abstract</span> +<span class="a">+The proliferation of generative AI systems has created new challenges for the Free and Open Source</span> +<span class="a">+Software (FOSS) community, particularly regarding how traditional copyleft principles should apply</span> +<span class="a">+when open source code is used to train AI models. This article introduces the Contextual Copyleft AI</span> +<span class="a">+(CCAI) license, a novel licensing mechanism that extends copyleft requirements from training data to</span> +<span class="a">+the resulting generative AI models. The CCAI license offers significant advantages, including</span> +<span class="a">+enhanced developer control, incentivization of open source AI development, and mitigation of open-</span> +<span class="a">+washing practices. This is demonstrated through a structured three-part evaluation framework that</span> +<span class="a">+examines (1) legal feasibility under current copyright law, (2) policy justification comparing traditional</span> +<span class="a">+software and AI contexts, and (3) synthesis of cross-contextual benefits and risks. However, the</span> +<span class="a">+increased risk profile of open source AI, particularly the potential for direct misuse, necessitates</span> +<span class="a">+complementary regulatory approaches to achieve an appropriate risk-benefit balance. The paper</span> +<span class="a">+concludes that when implemented within a robust regulatory environment focused on responsible AI</span> +<span class="a">+usage, the CCAI license provides a viable mechanism for preserving and adapting core FOSS</span> +<span class="a">+principles to the evolving landscape of generative AI development.</span> +<span class="a">+</span> +<span class="a">+Keywords</span> +<span class="a">+Copyleft, Open source, Free Software, Generative AI, Responsible AI</span> +<span class="a">+</span> +<span class="a">+ 1</span> +<span class="a">+</span> +1. Introduction +<span class="a">+With the recent increase in the number (Gozalo-Brizuela and Garrido-Merchan 2023) and power</span> +<span class="a">+(Bandi, Adapa, and Kuchi 2023) of generative AI systems, the open source community faces new</span> +<span class="a">+challenges in responding effectively. There is considerable discussion regarding the adoption of open</span> +<span class="a">+source generative AI models, particularly given the substantial opportunities and risks that accompany</span> +<span class="a">+making these systems open (Eiras, Petrov, Vidgen, Schroeder, et al. 2024; Floridi et al. 2025). Some</span> +<span class="a">+participants in the debate suggest shifting the focus away from open source generative AI, emphasizing</span> +<span class="a">+the need for responsible AI through behavioral use licensing instead (Contractor et al. 2022).</span> +<span class="a">+Questions persist about how the open source community should manage models trained on open</span> +<span class="a">+source code (a practice notably illustrated by GitHub’s CoPilot, as discussed in Section 4.2). This</span> +<span class="a">+dialogue focuses on preserving and adapting the rules, principles, and practices of the Free and Open</span> +<span class="a">+Source Software (FOSS) movement to navigate the evolving technical landscape introduced by</span> +<span class="a">+generative AI.</span> +<span class="a">+</span> +<span class="a">+ One way the FOSS movement has historically protected free software is through “copyleft”</span> +<span class="a">+licenses. These licenses require that derivative works be distributed under the same open source license</span> +<span class="a">+as the original work. This safeguards free and open source software because copyleft propagates</span> +<span class="a">+licenses from source material to derivative works, creating a viral effect where both original and</span> +<span class="a">+derivative works remain free and open (Phillips 2007). If attributes such as “freedom” and “openness”</span> +<span class="a">+are desirable for generative AI systems, then copyleft may provide a promising mechanism for the</span> +<span class="a">+FOSS community to uphold traditional software freedoms and extend their normative commitments</span> +<span class="a">+into the emerging field of generative AI.</span> +<span class="a">+</span> +<span class="a">+ This paper examines whether such an extension of copyleft principles is desirable and explores</span> +<span class="a">+potential mechanisms for achieving it. The Contextual Copyleft AI (CCAI) license is proposed, which may</span> +<span class="a">+cover both traditional software and generative AI contexts. Then, the challenges of evaluating licenses</span> +<span class="a">+that cross contextual boundaries are discussed, considering the legal and policy considerations in each</span> +<span class="a">+context. This leads to an argument about how such a license should be evaluated. Next, this structured</span> +<span class="a">+evaluation is completed for the CCAI license. The analysis concludes that the license could be</span> +<span class="a">+beneficial, particularly when it is used within a strong regulatory environment. The license provides</span> +<span class="a">+developers with greater control over how their code is used, encourages the development of open</span> +<span class="a">+source generative AI systems, and addresses concerns related to openwashing. Regulating the</span> +<span class="a">+responsible use of AI would address the risk-benefit imbalance inherent in copyleft, as highlighted by</span> +<span class="a">+the evaluation. This license highlights emerging cases where carefully constructed and thoughtfully</span> +<span class="a">+</span> +<span class="a">+ 2</span> +<span class="a">+</span> +evaluated copyleft licenses cross contextual boundaries, offering essential insights into how to preserve +<span class="a">+and adapt free and open source norms, principles, and practices in a changing technological landscape.</span> +<span class="a">+</span> +<span class="a">+2. The Contextual Copyleft AI (CCAI) License</span> +<span class="a">+This section proposes a new open source license that harnesses the features of copyleft to enable</span> +<span class="a">+developers to allow only open source generative AI models to train on their code. The copyleft scheme</span> +<span class="a">+enables copyright holders to permit users of their software to use, modify, and redistribute their code</span> +<span class="a">+in any way, provided that the modified or redistributed version is licensed under the same terms as</span> +<span class="a">+the original work (Stallman 2002). Fundamentally, copyleft is a mechanism that leverages the existing</span> +<span class="a">+copyright system to protect and promote the freedom of users to run, copy, distribute, study, change,</span> +<span class="a">+and improve software.</span> +<span class="a">+</span> +<span class="a">+ We will refer to the proposed license as the Contextual Copyleft AI (CCAI) license, a name that</span> +<span class="a">+draws on this license’s ability to cross technical and legal contexts. This ability necessitates a nuanced</span> +<span class="a">+evaluation of this license in the sections that follow. The full license text and a clause that can be added</span> +<span class="a">+to existing open source licenses are included in the appendix of this paper. The CCAI license consists</span> +<span class="a">+of three main components:</span> +<span class="a">+</span> +<span class="a">+ 1. Software freedom: Users can run the software, modify the code to fit their needs, and</span> +<span class="a">+ distribute copies of the software.</span> +<span class="a">+</span> +<span class="a">+ 2. Copyleft requirement: Any verbatim copy, modified version, derivative work, or model trained</span> +<span class="a">+ on this code must also be wholly licensed under CCAI with no additional restrictions. This</span> +<span class="a">+ includes a description of the training data, the code used to train and run the model, and the</span> +<span class="a">+ model’s parameters for generative AI models.1</span> +<span class="a">+</span> +<span class="a">+ 3. Distribution clarification: Any distributed CCAI software must include source code or provide</span> +<span class="a">+ a way to access it. Providing access to software over the network is considered equivalent to</span> +<span class="a">+ distributing the software.</span> +<span class="a">+</span> +<span class="a">+The CCAI license incorporates a copyleft, share-alike clause to propagate open source requirements</span> +<span class="a">+from training data to the AI model itself. It adheres to the open source norm of imposing terms on</span> +<span class="a">+distribution without using code to protect software freedom via an extended definition of “distribute.”</span> +<span class="a">+Furthermore, it enhances existing open source copyleft licenses by explicitly defining terms related to</span> +<span class="a">+</span> +<span class="a">+1 The description of the training data is inspired by the Open Source Initiative’s “Open Source AI” definition found at</span> +<span class="a">+https://opensource.org/ai/open-source-ai-definition. Description should generally include what data was used to train</span> +<span class="a">+the model, where the data originated, how it was selected and obtained, and discussion of any training or fliting methods.</span> +<span class="a">+</span> +<span class="a">+ 3</span> +<span class="a">+</span> +generative AI, such as including non-code aspects of an AI model in the definition of “entire work,” +<span class="a">+which helps enable developers to freely share their code online while preventing the unconstrained</span> +<span class="a">+training of AI models with their work. This is based on the premise that using code to train generative</span> +<span class="a">+AI models is not considered fair use, as discussed above. Note that adding the CCAI clause to an</span> +<span class="a">+existing open-source license effectively creates a new variant of the license. Care should be taken to</span> +<span class="a">+ensure compatibility with other open-source licenses, as some existing licenses (such as the GPL) do</span> +<span class="a">+not permit additional restrictions beyond their stated terms.</span> +<span class="a">+</span> +<span class="a">+ The first component of the license is software freedom, defined as allowing users to “run the</span> +<span class="a">+software, modify the code to fit their needs, and distribute copies of the software.” This establishes</span> +<span class="a">+the license’s goal and ensures that the usage rights of the code are granted to the software user</span> +<span class="a">+unconditionally, in accordance with all other open source licenses. This provision is essential for</span> +<span class="a">+making the license open source and supporting all four software freedoms.</span> +<span class="a">+</span> +<span class="a">+ The second component of the license is the copyleft requirement, which mandates that “any</span> +<span class="a">+verbatim copy, modified version, derivative work, or model trained on this code must also be wholly</span> +<span class="a">+licensed under CCAI, with no additional restrictions.” This requirement closely resembles the copyleft</span> +<span class="a">+stipulations in the GNU General Public License (GPL) and Affero General Public License (AGPL)</span> +<span class="a">+licenses,2 with the added categorization of AI models as part of derivative works. This definition would</span> +<span class="a">+impose share-alike terms on AI models if using code for training is not deemed fair use (if such training</span> +<span class="a">+were fair use, the original copyright holder’s license conditions could not apply to the model).</span> +<span class="a">+However, while the courts have yet to determine whether this training constitutes fair use, it is crucial</span> +<span class="a">+to include this clarification. The most significant addition to the fundamentals of the GPL and AGPL</span> +<span class="a">+is the second part of this component, which outlines the specific elements that must be licensed under</span> +<span class="a">+CCAI, particularly for AI models. The open sourcing must encompass “a description of the training</span> +<span class="a">+data, the code used to train and run the model, and the model’s parameters” under the same license</span> +<span class="a">+without additional restrictions. This definition was based on the Open Source Initiative’s definition of</span> +<span class="a">+Open Source AI.3 This definition helps combat open-washing, because it specifies the minimum</span> +<span class="a">+threshold for what counts as an open source model in the eyes of a copyleft license.</span> +<span class="a">+</span> +<span class="a">+ The third component of the license is the distribution clarification, a clause taken from the</span> +<span class="a">+AGPL license that categorizes accessing software over the network as distributing the software being</span> +<span class="a">+accessed. This clarification is essential to ensure that model users’ access via the internet is considered</span> +<span class="a">+</span> +<span class="a">+2 These are two popular OSS licenses, with AGPL targeted towards network-facing software.</span> +<span class="a">+3 https://opensource.org/ai/open-source-ai-definition</span> +<span class="a">+</span> +<span class="a">+ 4</span> +<span class="a">+</span> +“distributed” rather than being used by the developer. Otherwise, a model trained on data licensed +<span class="a">+under CCAI could allow users to access it through a website without requiring the model to be open</span> +<span class="a">+source, as these obligations come into effect upon distribution.</span> +<span class="a">+</span> +<span class="a">+3. Evaluating Licenses Propagating Between Contexts</span> +<span class="a">+Licenses that are designed to propagate between works in different technical and legal contexts pose</span> +<span class="a">+unique challenges in evaluating their legal and policy implications. This section argues that the</span> +<span class="a">+changing context of such licenses necessitates three components. First, a legal analysis is conducted</span> +<span class="a">+to determine whether the propagation mechanism is permissible within the existing legal framework.</span> +<span class="a">+Second, a policy analysis is performed to assess the individual benefits of copyleft within each context.</span> +<span class="a">+This analysis must then be synthesized to determine the overall desirability of such licenses. While the</span> +<span class="a">+necessity of the legal analysis is straightforward, the policy consideration is more nuanced. An open</span> +<span class="a">+source license may be desirable in its original context and be able to propagate to another context, but</span> +<span class="a">+the license may not be desirable in that new context. There are additional considerations, as the</span> +<span class="a">+primary justification for copyleft licenses is necessarily context-dependent; therefore, the rationale for</span> +<span class="a">+a license will change as it propagates between contexts. After introducing this framework for</span> +<span class="a">+evaluating licenses that propagate across different technical and legal contexts, it will be applied to the</span> +<span class="a">+CCAI license proposed above.</span> +<span class="a">+</span> +<span class="a">+ Technical contexts refer to sets of software that encompass work with similar risks of being</span> +<span class="a">+made open source. For instance, traditional software refers to software that is commonly open sourced</span> +<span class="a">+today, including desktop applications, computer science tools, and server software. The risks</span> +<span class="a">+associated with traditional open source software may include security vulnerabilities, dependency</span> +<span class="a">+management challenges, and concerns regarding project sustainability (Zajdel, Costa, and Mili, 2022).</span> +<span class="a">+Another context could be generative AI because the risks of releasing a generative AI model as open</span> +<span class="a">+source would be fundamentally different; the risks are focused on the harm that could be caused either</span> +<span class="a">+by humans using the model or by a misaligned model itself (Amodei et al. 2016). These technical</span> +<span class="a">+contexts may or may not exist within the same legal contexts. Typically, technologies operating in</span> +<span class="a">+various technical contexts are governed by the same overarching legal framework, although specific</span> +<span class="a">+regulatory requirements may differ.</span> +<span class="a">+</span> +<span class="a">+ The first component in evaluating a license designed to propagate between works in different</span> +<span class="a">+contexts is a legal evaluation of the feasibility of the propagation mechanism. While the necessity of</span> +<span class="a">+such analysis is self-evident, the examination itself may prove complex. This evaluation must primarily</span> +<span class="a">+</span> +<span class="a">+ 5</span> +<span class="a">+</span> +establish two elements: first, that the work to which the license propagates qualifies as a derivative of +<span class="a">+the original work under applicable copyright law; and second, that the incorporation of the original</span> +<span class="a">+work within the derivative work falls outside the scope of the fair use doctrine. If these conditions are</span> +<span class="a">+satisfied, it can generally be concluded that the license governing the original work may impose legal</span> +<span class="a">+obligations on its use within the derivative work, including the enforcement of share-alike provisions.</span> +<span class="a">+</span> +<span class="a">+ The second component is a policy evaluation of the use of copyleft in each context</span> +<span class="a">+individually. This must be done in each context individually because different technical contexts</span> +<span class="a">+present fundamentally different risk profiles that affect the cost-benefit analysis underlying the</span> +<span class="a">+justification of free software. These varying risks mean that the same copyleft license may be justified</span> +<span class="a">+in one context but unjustified in another, even when applied to functionally related software. The</span> +<span class="a">+conclusions from the separate policy evaluations must be considered jointly in determining whether</span> +<span class="a">+such a license should be used.</span> +<span class="a">+</span> +<span class="a">+ Copyleft licenses are politically justified by their ability to protect and promote free and open</span> +<span class="a">+source software.4 Thus, the justification for using copyleft within a specific context depends on the</span> +<span class="a">+rationale for using free and open source software in that context. If the use of free software generally</span> +<span class="a">+can’t be shown to be beneficial in a technical and legal context, then copyleft can’t be shown to be</span> +<span class="a">+beneficial either. Furthermore, the justification of free software must also consider the risks associated</span> +<span class="a">+with making a piece of software open source. Thus, when technical contexts change, the specifics of</span> +<span class="a">+the justification for free software, and therefore the justification for using copyleft, must change as</span> +<span class="a">+well. The overarching argument in support of free software may remain unchanged; however, the</span> +<span class="a">+specifics of the argument must evolve either way. For instance, copyleft on a traditional piece of</span> +<span class="a">+software may be justified by a pragmatic argument regarding the quality of software produced by free</span> +<span class="a">+and open source communities. If this license propagates to a generative AI system, the justification</span> +<span class="a">+may or may not change, remaining pragmatic or taking on moral terms instead. However, the specifics</span> +<span class="a">+of the argument must change due to the different fundamental risks associated with making the</span> +<span class="a">+software open source. Therefore, the policy evaluation must be conducted individually for each</span> +<span class="a">+technical context.</span> +<span class="a">+</span> +<span class="a">+ The third component is a policy analysis of copyleft propagation as a whole. While the policy</span> +<span class="a">+analysis justifying the use of copyleft and open source must be conducted individually in each context,</span> +<span class="a">+the results should be considered collectively to make a final determination as to whether the license</span> +<span class="a">+</span> +<span class="a">+4 https://www.gnu.org/licenses/licenses.html</span> +<span class="a">+</span> +<span class="a">+ 6</span> +<span class="a">+</span> +should be used or not. This synthesis must evaluate the specific benefits and drawbacks of the +<span class="a">+propagation mechanism itself, including: whether it empowers developers by giving them appropriate</span> +<span class="a">+control over how their work is used across contexts; whether it creates positive incentives for open</span> +<span class="a">+source development in the new context; whether it helps maintain the integrity of open source</span> +<span class="a">+principles as they extend to new technologies; and whether these benefits of cross-context propagation</span> +<span class="a">+outweigh any tensions created by applying the same license across different risk profiles. The synthesis</span> +<span class="a">+should conclude with a holistic assessment of whether the propagation serves the overarching goals</span> +<span class="a">+of the FOSS community.</span> +<span class="a">+</span> +<span class="a">+4. Contextual Copyleft from Traditional Software to Generative AI</span> +<span class="a">+In this section, we evaluate the proposed CCIA license using the three-part analysis described in the</span> +<span class="a">+previous section. First, we examine the legal mechanisms through which copyleft licenses could extend</span> +<span class="a">+from training data to AI models, focusing on the critical question of whether AI training constitutes</span> +<span class="a">+fair use under US copyright law. This legal analysis proceeds under the assumption that such training</span> +<span class="a">+does not automatically qualify as fair use, allowing us to explore how licensing terms might apply.</span> +<span class="a">+</span> +<span class="a">+ Second, we evaluate the use of copyleft licenses in each context individually—traditional</span> +<span class="a">+software and generative AI—to understand how the justification for these licenses changes across</span> +<span class="a">+contexts. This comparative analysis reveals significant differences in the risk profiles between</span> +<span class="a">+traditional open source software and open source AI, particularly regarding the potential for direct</span> +<span class="a">+misuse and harm generation.</span> +<span class="a">+</span> +<span class="a">+ Finally, we synthesize these analyses to demonstrate that a copyleft license that propagates to</span> +<span class="a">+AI systems offers three key benefits: it grants developers greater agency over how their code is used</span> +<span class="a">+in AI development, stimulates the creation of open source generative AI systems, and combats “open-</span> +<span class="a">+washing” practices. Practical enforcement of the CCAI license against AI models trained on publicly</span> +<span class="a">+available data might be challenging due to difficulties in detecting unauthorized usage. However, even</span> +<span class="a">+in cases where enforcement is challenging, the existence of clearly articulated license terms can serve</span> +<span class="a">+as an effective deterrent against non-compliant use by law-abiding entities. We also acknowledge that</span> +<span class="a">+the increased risks associated with open source AI, particularly the potential for direct misuse, require</span> +<span class="a">+consideration of complementary regulatory approaches. We conclude by arguing that responsible AI</span> +<span class="a">+regulation can work alongside copyleft licensing to better align the risks and benefits of free software</span> +<span class="a">+principles in the context of generative AI.</span> +<span class="a">+</span> +<span class="a">+ 7</span> +<span class="a">+</span> +4.1. Legal Analysis +<span class="a">+Consider the case of a developer trying to prevent their publicly published work from being used to</span> +<span class="a">+train generative AI models. What legal tools are available to this person, and under what circumstances</span> +<span class="a">+can they be used?</span> +<span class="a">+</span> +<span class="a">+ Under United States (US) copyright law, authors of original works receive several exclusive</span> +<span class="a">+rights, codified in 17 U.S.C. § 106. These include the rights to reproduce the work, prepare derivative</span> +<span class="a">+works based on it, distribute copies to the public, and publicly perform or display the work. However,</span> +<span class="a">+these rights are subject to statutory limitations, including exceptions such as fair use and compulsory</span> +<span class="a">+licensing schemes that serve broader social and economic interests. Copyright automatically applies to</span> +<span class="a">+original works when they are fixed in a tangible medium, including digital formats such as online</span> +<span class="a">+content. This means that any original code published online is immediately protected. Licenses specify</span> +<span class="a">+the rights that are legally reserved for the copyright holder and grant other rights to users of the work,</span> +<span class="a">+often with attached terms and conditions. In the case of copyleft, this arrangement is reversed via a</span> +<span class="a">+license, with all rights granted to the user on the condition that derivative works are released under</span> +<span class="a">+the same license.</span> +<span class="a">+</span> +<span class="a">+ Fair use is the primary limitation of licenses serving as a method to grant developers greater</span> +<span class="a">+control over how their code is used. Licenses, as contracts between the copyright owner and the</span> +<span class="a">+software user, can only place terms on rights reserved for the copyright holder. Under US copyright</span> +<span class="a">+law, the doctrine of fair use, determined by a set of four standards, allows an existing work to be used</span> +<span class="a">+to create a new work such that it is no longer considered a derivative work of the original if the new</span> +<span class="a">+work is sufficiently “transformative.” Therefore, the original copyright holder cannot impose</span> +<span class="a">+conditions on how their work is used if the new work qualifies as fair use.</span> +<span class="a">+</span> +<span class="a">+ Regarding generative AI, if it becomes established through the courts or legislation that using</span> +<span class="a">+code to train a model is always fair use of that training data, the discussion of licenses in this context</span> +<span class="a">+becomes irrelevant. Licenses would be unable to restrict what models can train on, given that publicly</span> +<span class="a">+available code is unrestricted. However, there is uncertainty about whether training models on code</span> +<span class="a">+is, or should be, regarded as fair use (Charlesworth 2024). For instance, some argue that the</span> +<span class="a">+substantially similar relationship between the input and output of a model, the commercial purpose</span> +<span class="a">+of many AI, models, and how models can replace the source material in the market suggest that</span> +<span class="a">+training on copyrighted material may not qualify as fair use (Shen 2024).</span> +<span class="a">+</span> +<span class="a">+ The May 2025 US Copyright Office report provides crucial (although non-binding) guidance</span> +<span class="a">+on this uncertainty. The report concludes that training AI models on copyrighted works “clearly</span> +<span class="a">+</span> +<span class="a">+ 8</span> +<span class="a">+</span> +implicates the right of reproduction,” explicitly rejecting any blanket fair use protection for AI training +<span class="a">+(US Copyright Office 2025). The Office emphasized that the reproduction right is triggered when</span> +<span class="a">+copyrighted works are copied into training datasets, regardless of whether those copies are retained</span> +<span class="a">+after the training process is complete. This finding directly challenges the argument that AI training</span> +<span class="a">+constitutes non-expressive use, exempt from copyright concerns. The report further notes that</span> +<span class="a">+commercial uses creating competing content are less likely to qualify as fair use, while research and</span> +<span class="a">+non-commercial uses receive more favorable treatment—a distinction particularly relevant for</span> +<span class="a">+commercial code generation models.</span> +<span class="a">+</span> +<span class="a">+ With the assumption that this sort of training is not fair use, we can hypothesize that</span> +<span class="a">+developers can use licenses to restrict how their code is used to train generative AI models. In this</span> +<span class="a">+case, generative AI models trained on code could plausibly be considered derivative works of the</span> +<span class="a">+training data, although this interpretation remains legally unsettled, as the exact code from the training</span> +<span class="a">+data is not present in the model’s codebase and is never technically executed by the model. A similar</span> +<span class="a">+conclusion as to the ability of open source licenses to propagate to AI models has been found where</span> +<span class="a">+Benhamou argues “when the AI model incorporates open code into its program or open data into its</span> +<span class="a">+training dataset, there is a risk of propagation, i.e. a risk that the entire model or the input data become</span> +<span class="a">+fully open, as the AI model or the input can be considered as derivative of the open code or data,</span> +<span class="a">+whose license requires derivatives to remain open” (Benhamou 2024).</span> +<span class="a">+</span> +<span class="a">+ The uncertainty surrounding whether the use of open source code as training data constitutes</span> +<span class="a">+fair use warrants consideration in its own right. The current confusion in the legal landscape naturally</span> +<span class="a">+leads developers to restrict licenses defensively, potentially hindering both AI innovation and open</span> +<span class="a">+source collaboration. A clear, purpose-built license can overcome this confusion by providing explicit</span> +<span class="a">+guidance that operates effectively under multiple legal scenarios. If training is not deemed fair use,</span> +<span class="a">+then licenses become the primary mechanism for controlling the use of AI training. A license</span> +<span class="a">+specifically addressing AI training for code generation models would provide clear guidance to all</span> +<span class="a">+parties, potentially increasing the pool of code available for training while respecting developer</span> +<span class="a">+preferences. However, if courts ultimately determine that training AI models constitutes fair use, such</span> +<span class="a">+licensing terms would have no legal force. And it is worth noting that jurisdictions outside the US,</span> +<span class="a">+such as the EU, provide explicit text and data mining (TDM) exceptions, which permit data mining</span> +<span class="a">+unless right-holders explicitly opt-out through licensing terms. Thus, the CCAI license terms could</span> +<span class="a">+effectively serve as an explicit opt-out of such statutory exceptions under EU law. Therefore, we see</span> +<span class="a">+</span> +<span class="a">+ 9</span> +<span class="a">+</span> +legal value in such a license, regardless of how the US courts ultimately rule on the question of fair +<span class="a">+use.</span> +<span class="a">+</span> +<span class="a">+4.2. Policy Analysis in Each Context</span> +<span class="a">+The FOSS community generally agrees on the benefits of FOSS, which, according to the community,</span> +<span class="a">+include reliability, security, and low cost (AlMarzouq et al. 2005). However, sub-movements of the</span> +<span class="a">+community disagree on the intellectual justification for FOSS. The free software (FS) movement</span> +<span class="a">+presents moral claims to advocate for free software as defined above. Then, there is the open source</span> +<span class="a">+(OS) movement, which offers pragmatic arguments regarding the value of free software. The</span> +<span class="a">+differences can be summed up as “open source is a development methodology; free software is a</span> +<span class="a">+social movement,” but their “practical recommendations” are largely similar (Stallman 2002).</span> +<span class="a">+</span> +<span class="a">+ The FS movement argues for free software based on what Richard Stallman describes as the</span> +<span class="a">+“Golden Rule” applied to software development. He argues that proprietary software is destructive</span> +<span class="a">+insofar as it “reduces the amount and the ways that a program can be used” and, as a result, reduces</span> +<span class="a">+the wealth humanity derives from the program (Stallman 2002). He ties this to Kantian ethics, arguing</span> +<span class="a">+that a good citizen does not use destructive means, such as proprietary software, to become wealthier.</span> +<span class="a">+With this, he concludes that “programmers have the duty to encourage others to share, redistribute,</span> +<span class="a">+study, and improve the software we write” (Stallman 2002). Another way that this has been formulated</span> +<span class="a">+is to start from the premise that software has permeated society, and, under a proprietary model,</span> +<span class="a">+software producers can control the availability of and access to software. As a result, “we are beholden</span> +<span class="a">+to these producers to run our modern society and are theoretically at their whim” (Heyns 2012).</span> +<span class="a">+Proponents of FS argue that this outcome is undesirable. Instead, software should be regarded as</span> +<span class="a">+infrastructure, as it is essential for societal participation and engagement. Thus, the assertion is made</span> +<span class="a">+that society should regulate access to the software “infrastructure” rather than software companies.</span> +<span class="a">+This approach helps prevent what has been termed “the digital divide,” wherein select groups of</span> +<span class="a">+people have access to the broad digital ecosystem (James 2003).</span> +<span class="a">+</span> +<span class="a">+ The OS movement advocates for free software from a strictly pragmatic or utilitarian</span> +<span class="a">+framework, arguing that free software is more beneficial to society than proprietary software. Software</span> +<span class="a">+plays a critical role today, making society interested in the quality and reliability of the software that is</span> +<span class="a">+produced and used. OS advocates argue that making source code freely available online for public</span> +<span class="a">+collaboration results in high-quality and highly reliable software. The OS community generally posits</span> +<span class="a">+that this is due to the ability to peer review software and provide more timely fixes because more</span> +<span class="a">+</span> +<span class="a">+ 10</span> +<span class="a">+</span> +developers can review source code in search of bugs and security risks. When these issues are +<span class="a">+identified, a distributed community can work together to create solutions, rather than a select group</span> +<span class="a">+within a specific company addressing the problem. The idea that a distributed community can lead to</span> +<span class="a">+better software outcomes is summed up in the famous Linus’s Law: “Given enough eyeballs, all bugs</span> +<span class="a">+are shallow” (Raymond 1999). OS is distinct from FS in that it does not make moral claims, but is</span> +<span class="a">+based on pragmatic claims regarding which development model will yield the highest quality and most</span> +<span class="a">+reliable software.</span> +<span class="a">+</span> +<span class="a">+ The primary risks of releasing certain pieces of traditional software as open source include</span> +<span class="a">+security vulnerabilities and software misuse (Zajdel, Costa, and Mili 2022). Regarding security</span> +<span class="a">+vulnerabilities, the risk of malicious actors discovering and exploiting these vulnerabilities can be</span> +<span class="a">+mitigated by allowing multiple developers to review the code and identify and fix bugs. Thus, “open</span> +<span class="a">+source does not pose any significant barriers to security but rather reinforces sound security practices</span> +<span class="a">+by involving many people that expose bugs quickly and offers side-effects that provide customers and</span> +<span class="a">+the community with concrete examples of reusable, secure, and working code” (Clarke, Dorwin, and</span> +<span class="a">+Nash 2009).</span> +<span class="a">+</span> +<span class="a">+ Moving from the traditional software context to generative AI, the risk profile changes. The</span> +<span class="a">+security risks of open source AI are non-negligible, with a survey revealing that 10% of IT decision-</span> +<span class="a">+makers have accidentally installed malicious code in their organizations through the use of open source</span> +<span class="a">+AI tools.5 Furthermore, the risk of actors using the code for malicious purposes is a concern that is</span> +<span class="a">+less directly countered by the benefits of open source software and is more challenging to mitigate.</span> +<span class="a">+Once open source software is out in the world, there is by definition no way that the developers can</span> +<span class="a">+control its use; while Meta’s Llama terms of service ostensibly prohibit its use for military purposes,</span> +<span class="a">+researchers associated with the Chinese People’s Liberation Army were still able to use it to create a</span> +<span class="a">+tool to gather and synthesize intelligence (Wilson and Hine 2025).</span> +<span class="a">+</span> +<span class="a">+ Additional risks associated with open source AI include misuse, where software is used in a</span> +<span class="a">+technically unintended manner to inflict harm. When it comes to OSS related to generative AI, there</span> +<span class="a">+are several pathways to explicit harm, including the exploitation of security vulnerabilities and the</span> +<span class="a">+ability to create harmful content, particularly given the challenges of rolling back or updating models</span> +<span class="a">+(Eiras, Petrov, Vidgen, Witt, et al. 2024). The possibility of generating harmful content or using a</span> +<span class="a">+foundation model for malicious activities is particularly concerning given the nature of such misuse.</span> +<span class="a">+</span> +<span class="a">+5 https://www.anaconda.com/lp/state-of-enterprise-open-source-ai</span> +<span class="a">+</span> +<span class="a">+ 11</span> +<span class="a">+</span> +This type of misuse is primarily direct, contrasting with actor-dependent misuse seen in traditional +<span class="a">+software. For example, if a foundation model is fine-tuned on a company’s emails, it may generate</span> +<span class="a">+highly effective phishing emails, posing a more significant threat to unsuspecting employees. This</span> +<span class="a">+scenario differs fundamentally from using an open source email client to send phishing emails. Here,</span> +<span class="a">+the open source technology in the case of generative AI is being used to amplify harm. In contrast, in</span> +<span class="a">+the traditional software case, the open source software operates as intended but is misused by the</span> +<span class="a">+actor to inflict damage. The risk of this explicit misuse in generative AI is significantly higher compared</span> +<span class="a">+to traditional software.</span> +<span class="a">+</span> +<span class="a">+4.3 Synthesizing the Analysis</span> +<span class="a">+The legal analysis has shown that the CCAI license can propagate from training data to a generative</span> +<span class="a">+AI model itself, while the policy analysis has shown strong support for the open-sourcing of traditional</span> +<span class="a">+software, highlighting both substantial benefits and risks in the context of open source AI. The</span> +<span class="a">+particular risk of direct misuse stands out as a significant concern, particularly in the context of open</span> +<span class="a">+source AI, compared to traditional software, which could complicate the justification for open source</span> +<span class="a">+approaches in the generative AI context. In this section, we will take these insights and show that the</span> +<span class="a">+CCAI license could benefit both the open source and AI development communities when used in</span> +<span class="a">+particular regulatory schemes. The promise such a license holds for both communities is first argued,</span> +<span class="a">+and then a discussion of regulatory schemes follows.</span> +<span class="a">+</span> +<span class="a">+ This type of copyleft license offers three key advantages. First, it would give developers</span> +<span class="a">+enhanced control over how their code is incorporated into generative AI development. Second, it</span> +<span class="a">+would promote the development of genuinely open source generative AI systems. Third, it would help</span> +<span class="a">+combat “open-washing” practices that dilute the real benefits that open source generative AI systems</span> +<span class="a">+can provide.</span> +<span class="a">+</span> +<span class="a">+ First, a copyleft license that propagates from traditional software to generative AI systems</span> +<span class="a">+provides an important mechanism to give developers greater control over how their code is used in</span> +<span class="a">+AI systems. Today, when developers open source their code, they lose control over what types of</span> +<span class="a">+generative AI models are trained on their code and for what purposes those AI models are employed.</span> +<span class="a">+On the one hand, this is a feature, not a bug. Open source licenses are explicitly designed to allow the</span> +<span class="a">+unrestricted use of code, enabling others to use the original work in new and innovative ways. At the</span> +<span class="a">+same time, developers often do not want their work to contribute to nefarious ends, which could</span> +<span class="a">+occur if their code is used to train a generative AI model intended to cause harm, for example. This</span> +<span class="a">+</span> +<span class="a">+ 12</span> +<span class="a">+</span> +has led some developers to add “no AI” clauses to existing licenses to prohibit using their code for +<span class="a">+training an AI model.6</span> +<span class="a">+</span> +<span class="a">+ The tension within the open source community regarding the balance between freely sharing</span> +<span class="a">+software and caution towards unrestricted training on such software is evident in the backlash against</span> +<span class="a">+the release of CoPilot. GitHub CoPilot is an AI model developed specifically to generate code and,</span> +<span class="a">+consequently, requires a vast amount of code for its training data. This prompted GitHub to use code</span> +<span class="a">+from numerous open source repositories, asserting that the training of their model constituted fair use</span> +<span class="a">+and, therefore, the licensing terms of the code did not apply. Many in the FOSS community disagreed,</span> +<span class="a">+leading to a boycott of GitHub by many open source developers and a class-action lawsuit against</span> +<span class="a">+GitHub, Microsoft, OpenAI, and others involved in creating CoPilot (Class Action Complaint 3:22-</span> +<span class="a">+cv-06823). Some even argue that foundational models like CoPilot may discourage developers from</span> +<span class="a">+contributing to open source projects, as these models “exploit the principle of solidarity operating</span> +<span class="a">+within the movement” and have the potential to undermine the movement altogether (Novobilská</span> +<span class="a">+2023). An open source license with a copyleft clause would specify that it propagates from training</span> +<span class="a">+data to generative AI models, providing open source developers a middle ground between allowing</span> +<span class="a">+unconstrained training and limiting innovation and openness by restricting all training.</span> +<span class="a">+</span> +<span class="a">+ Second, introducing and adopting a copyleft license that propagates from training data to AI</span> +<span class="a">+models would lead to more open source generative AI models—an outcome that the open source</span> +<span class="a">+community supports.7 It is well known that training generative AI models requires vast amounts of</span> +<span class="a">+data, so a model designed to generate code (such as GitHub’s CoPilot) would need substantial code</span> +<span class="a">+to train on. Open source repositories are currently the solution for model developers. Suppose such a</span> +<span class="a">+license were widely adopted within the FOSS community. Of course, the effectiveness of the CCAI</span> +<span class="a">+license depends heavily on its widespread adoption. If only a small subset of developers adopts it,</span> +<span class="a">+proprietary AI developers may simply avoid CCAI-licensed code. A license of this kind would outline</span> +<span class="a">+which portion of the open source code online is available for use by open source models during</span> +<span class="a">+training. This would create an incentive for the open sourcing of new generative AI models that</span> +<span class="a">+produce code, as it provides open source model developers with access to training data that proprietary</span> +<span class="a">+models cannot use. The incentivization and creation of open source generative AI models offer new</span> +<span class="a">+opportunities for external oversight, technical innovation, and decentralized control over AI</span> +<span class="a">+development compared to closed-source models (Seger et al. 2023).</span> +<span class="a">+</span> +<span class="a">+6 https://github.com/non-ai-licenses/non-ai-licenses</span> +<span class="a">+7 https://opensource.org/ai/open-source-ai-definition</span> +<span class="a">+</span> +<span class="a">+ 13</span> +<span class="a">+</span> + Third, a copyleft license of this type could provide more explicit, enforceable definitions for +<span class="a">+open source AI, helping to combat “open-washing.” Open-washing refers to AI models that claim to</span> +<span class="a">+be open source by publicly sharing only one component, such as the model weights (Liesenfeld and</span> +<span class="a">+Dingemanse 2024). While this may allow users to download and run the model on their computers, it</span> +<span class="a">+fails to convey the comprehensive understanding of software freedom that the open source</span> +<span class="a">+community has cultivated; crucial contexts, including the code that trained the model and details of</span> +<span class="a">+the training data, are absent. The current language in copyleft licenses mandates that the “entire work”</span> +<span class="a">+be shared under the same license as the original, not just the portion of the software copied from the</span> +<span class="a">+original work. From a model developer’s perspective, the “entire work” could encompass not only the</span> +<span class="a">+model weights but also the underlying model architecture, supporting code, and details about the</span> +<span class="a">+training data. The interpretation of “entire work” as it applies to generative AI could broaden the</span> +<span class="a">+required components of an AI system that must be open to tackle the open-washing observed in the</span> +<span class="a">+AI development community effectively.</span> +<span class="a">+</span> +<span class="a">+ These three advantages of the CCAI license must be considered in light of the above legal and</span> +<span class="a">+policy analysis. Specifically, the risk of direct misuse of open source AI is that it is used for malicious</span> +<span class="a">+purposes. The challenge is that open source licenses must not restrict how a technology can be used,</span> +<span class="a">+as defined by the principles of open source.8 However, while it can’t be prohibited, there is precedent</span> +<span class="a">+within the FOSS community resisting the misuse of open source software. For instance, a former</span> +<span class="a">+developer at Chef, a computer management software company, created open source tools that helped</span> +<span class="a">+customers work with Chef’s software. After leaving the company, he discovered a contract between</span> +<span class="a">+Chef and the US Immigration and Customs Enforcement (ICE), prompting him to remove the code</span> +<span class="a">+with a note stating, “I have a moral and ethical obligation to prevent my source [code] from being</span> +<span class="a">+used for evil.”9 This decision caused an outage for multiple customers, leading Chef to republish an</span> +<span class="a">+older version of the open source tool’s source code on the company’s corporate GitHub account, an</span> +<span class="a">+action permitted by the software freedom provisions of the original license. However, it should be</span> +<span class="a">+noted that the developer was able to protest Chef’s collaboration with ICE because he had authority</span> +<span class="a">+over that code.</span> +<span class="a">+</span> +<span class="a">+ One possible way to mitigate the risks of open source generative AI would be to introduce a</span> +<span class="a">+responsible AI use regulation that limits how open source models can be used to cause direct harm.</span> +<span class="a">+For instance, part of the EU AI Act prevents AI systems from using subliminal, manipulative, or</span> +<span class="a">+</span> +<span class="a">+8 https://opensource.org/osd</span> +<span class="a">+9 https://www.zdnet.com/article/no-source-code-for-evil-developers-pressure-chef-software-to-cut-ties-with-ice/</span> +<span class="a">+</span> +<span class="a">+ 14</span> +<span class="a">+</span> +deceptive techniques that distort a person’s behavior and impair decision-making, causing significant +<span class="a">+harm, or exploiting individuals or groups based on age, disability, or socio-economic conditions to</span> +<span class="a">+influence behaviors in a harmful way (European Union 2024). Although these provisions have been</span> +<span class="a">+criticized for a lack of specificity (Montag and Finck 2024), similar restrictions in the US could help</span> +<span class="a">+mitigate the risk of misuse of open source generative AI systems. This would help rebalance the risks</span> +<span class="a">+and benefits of open source generative AI closer to those of traditional open source software, allowing</span> +<span class="a">+for a stronger endorsement of copyleft in both contexts.</span> +<span class="a">+</span> +<span class="a">+5. Conclusion</span> +<span class="a">+The analysis presented in this article demonstrates that the Contextual Copyleft AI (CCAI) license</span> +<span class="a">+represents a promising approach to extending traditional copyleft principles into the domain of</span> +<span class="a">+generative AI development. Through our structured evaluation framework, we have determined that</span> +<span class="a">+such a license is legally feasible under current copyright law, assuming that AI training does not</span> +<span class="a">+constitute fair use, and offers substantial benefits to both the open source and AI development</span> +<span class="a">+communities. The license addresses three critical challenges facing the FOSS community in the age of</span> +<span class="a">+generative AI: providing developers with meaningful control over how their code is incorporated into</span> +<span class="a">+AI systems, creating incentives for genuinely open source AI development, and combating the dilution</span> +<span class="a">+of open source principles through open-washing practices. These advantages align with the</span> +<span class="a">+fundamental goals of the FOSS movement while adapting to the unique characteristics of generative</span> +<span class="a">+AI technologies.</span> +<span class="a">+</span> +<span class="a">+ However, our policy analysis reveals that the risk profile of open source generative AI differs</span> +<span class="a">+significantly from that of traditional software, particularly regarding the potential for direct misuse,</span> +<span class="a">+where AI systems can be employed to generate harmful content or amplify malicious activities. This</span> +<span class="a">+increased risk profile challenges the traditional cost-benefit calculus that has historically justified</span> +<span class="a">+copyleft licensing in software development. The analysis suggests that while the benefits of open</span> +<span class="a">+source AI remain substantial—including enhanced oversight, innovation, and decentralized control—</span> +<span class="a">+the heightened risks require careful consideration and potentially complementary regulatory</span> +<span class="a">+mechanisms to achieve an appropriate balance.</span> +<span class="a">+</span> +<span class="a">+ The path forward for implementing the CCAI license depends critically on the development</span> +<span class="a">+of comprehensive regulatory frameworks that address the responsible use of AI systems. Such</span> +<span class="a">+regulations, exemplified by provisions in the EU AI Act that restrict manipulative and exploitative AI</span> +<span class="a">+applications, could help mitigate the direct misuse risks that complicate the justification for open</span> +<span class="a">+</span> +<span class="a">+ 15</span> +<span class="a">+</span> +source AI. CCAI alone does not eliminate misuse risks—it ensures openness but cannot prevent +<span class="a">+malicious use of openly released models. Thus, its practical effectiveness depends significantly on</span> +<span class="a">+complementary regulatory measures addressing responsible use of AI. Within such a regulatory</span> +<span class="a">+environment, the CCAI license emerges as a valuable tool for preserving the core values of software</span> +<span class="a">+freedom while adapting to the realities of modern AI development. This analysis thus contributes to</span> +<span class="a">+the broader discourse on maintaining the integrity and relevance of FOSS principles as technology</span> +<span class="a">+continues to evolve, demonstrating that thoughtful adaptation of traditional licensing mechanisms can</span> +<span class="a">+help navigate the complex challenges posed by generative AI while upholding the fundamental</span> +<span class="a">+commitments of the open source community.</span> +<span class="a">+</span> +<span class="a">+ 16</span> +<span class="a">+</span> +Appendix A: The CCAI License +<span class="a">+The following is the text of the CCAI license designed to propagate from training data to generative</span> +<span class="a">+AI models.</span> +<span class="a">+</span> +<span class="a">+ Contextual Copyleft AI (CCAI) License v1.0</span> +<span class="a">+</span> +<span class="a">+ Copyright (c) [year] [author]</span> +<span class="a">+</span> +<span class="a">+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated</span> +<span class="a">+ documentation files (the “Software”), to use, copy, modify, merge, publish, distribute, and/or sublicense copies</span> +<span class="a">+ of the Software, subject to the following conditions:</span> +<span class="a">+</span> +<span class="a">+ 1. Software Freedom.</span> +<span class="a">+ Users are granted the freedom to run, study, modify, and distribute the Software, including modified versions,</span> +<span class="a">+ for any purpose.</span> +<span class="a">+</span> +<span class="a">+ 2. Copyleft Requirement.</span> +<span class="a">+ Any distribution, publication, or making available of the Software — including exact copies, modified versions,</span> +<span class="a">+ or derivative works — must be licensed in full under the terms of this CCAI License without imposing any</span> +<span class="a">+ further restrictions. This requirement also applies to any model, dataset, or system that is trained on, derived</span> +<span class="a">+ from, or incorporates the Software or its outputs. In the case of generative AI models, the obligation to apply</span> +<span class="a">+ this license includes, at minimum, the disclosure of the model’s source code, a meaningful description of the</span> +<span class="a">+ training data, and the model’s parameters, weights, and architecture.</span> +<span class="a">+</span> +<span class="a">+ 3. Distribution Clarification.</span> +<span class="a">+ Providing access to the Software or any covered model over a network or online service shall be considered</span> +<span class="a">+ distribution and triggers the obligations of this License.</span> +<span class="a">+</span> +<span class="a">+ 4. No Warranty.</span> +<span class="a">+ The Software is provided “as is”, without warranty of any kind.</span> +<span class="a">+</span> +<span class="a">+ 5. Preservation of License Notice.</span> +<span class="a">+ Copies, modifications, and derivative works must retain this license notice.</span> +<span class="a">+</span> +<span class="a">+Appendix B: The CCAI Clause</span> +<span class="a">+The following is the text of the CCAI clause that can be added to existing open source licenses to</span> +<span class="a">+propagate the license from training data to generative AI models.</span> +<span class="a">+</span> +<span class="a">+ Contextual Copyleft AI (CCAI) Clause</span> +<span class="a">+</span> +<span class="a">+ If this software is used to train, fine-tune, or otherwise generate any machine learning model or generative AI</span> +<span class="a">+ system, any resulting model, dataset, or system must be released under terms no more restrictive than this license.</span> +<span class="a">+ This includes the requirement to make available the training code, a description of the data used in training, and</span> +<span class="a">+ the model’s parameters and architecture. Providing access to such a model or its outputs via a network is</span> +<span class="a">+ considered distribution and triggers this obligation.</span> +<span class="a">+</span> +<span class="a">+ 17</span> +<span class="a">+</span> +References +<span class="a">+</span> +<span class="a">+“1-0-Github_complaint.Pdf.” n.d. Accessed February 19, 2025.</span> +<span class="a">+ https://githubcopilotlitigation.com/pdf/07074/1-0-github_complaint.pdf.</span> +<span class="a">+</span> +<span class="a">+AlMarzouq, Mohammad, Li Zheng, Guang Rong, and Varun Grover. 2005. “Open Source:</span> +<span class="a">+ Concepts, Benefits, and Challenges.” Communications of the Association for Information Systems 16.</span> +<span class="a">+ https://doi.org/10.17705/1CAIS.01637.</span> +<span class="a">+</span> +<span class="a">+Amodei, Dario, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané.</span> +<span class="a">+ 2016. “Concrete Problems in AI Safety.” arXiv.</span> +<span class="a">+ https://doi.org/10.48550/arXiv.1606.06565.</span> +<span class="a">+</span> +<span class="a">+Bandi, Ajay, Pydi Venkata Satya Ramesh Adapa, and Yudu Eswar Vinay Pratap Kumar Kuchi. 2023.</span> +<span class="a">+ “The Power of Generative AI: A Review of Requirements, Models, Input–Output Formats,</span> +<span class="a">+ Evaluation Metrics, and Challenges.” Future Internet 15 (8): 260.</span> +<span class="a">+ https://doi.org/10.3390/fi15080260.</span> +<span class="a">+</span> +<span class="a">+Benhamou, Yaniv. 2024. “Open Source AI: Does the Copyleft Clause Propagate to Proprietary AI</span> +<span class="a">+ Models? Revisiting the Definition of Derivatives in the AI-Context.” SSRN Scholarly Paper.</span> +<span class="a">+ Rochester, NY: Social Science Research Network. https://doi.org/10.2139/ssrn.4859623.</span> +<span class="a">+</span> +<span class="a">+Charlesworth, Jacqueline. 2024. “Generative AI’s Illusory Case for Fair Use.” SSRN Scholarly Paper.</span> +<span class="a">+ Rochester, NY: Social Science Research Network. https://doi.org/10.2139/ssrn.4924997.</span> +<span class="a">+</span> +<span class="a">+Clarke, Russell, David Dorwin, and Rob Nash. 2009. “Is Open Source Software More Secure?”</span> +<span class="a">+Contractor, Danish, Daniel McDuff, Julia Katherine Haines, Jenny Lee, Christopher Hines, Brent</span> +<span class="a">+</span> +<span class="a">+ Hecht, Nicholas Vincent, and Hanlin Li. 2022. “Behavioral Use Licensing for Responsible</span> +<span class="a">+ AI.” In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, 778–</span> +<span class="a">+ 88. FAccT ‘22. New York, NY, USA: Association for Computing Machinery.</span> +<span class="a">+ https://doi.org/10.1145/3531146.3533143.</span> +<span class="a">+Eiras, Francisco, Aleksandar Petrov, Bertie Vidgen, Christian Schroeder, Fabio Pizzati, Katherine</span> +<span class="a">+ Elkins, Supratik Mukhopadhyay, et al. 2024. “Risks and Opportunities of Open-Source</span> +<span class="a">+ Generative AI.” arXiv. https://doi.org/10.48550/arXiv.2405.08597.</span> +<span class="a">+Eiras, Francisco, Aleksandar Petrov, Bertie Vidgen, Christian Schroeder de Witt, Fabio Pizzati,</span> +<span class="a">+ Katherine Elkins, Supratik Mukhopadhyay, et al. 2024. “Near to Mid-Term Risks and</span> +<span class="a">+ Opportunities of Open-Source Generative AI.” arXiv.</span> +<span class="a">+ https://doi.org/10.48550/arXiv.2404.17047.</span> +<span class="a">+European Union. 2024. “Regulation - EU - 2024/1689 - EN - EUR-Lex.” 2024. https://eur-</span> +<span class="a">+ lex.europa.eu/eli/reg/2024/1689/oj/eng.</span> +<span class="a">+Floridi, Luciano, Carlotta Buttaboni, Emmie Hine, Claudio Novelli, Tyler Schroder, and Grant</span> +<span class="a">+ Shanklin. 2025. “Open-Source AI Made in the EU: Why It Is a Good Idea.” SSRN Scholarly</span> +<span class="a">+ Paper. Rochester, NY: Social Science Research Network.</span> +<span class="a">+ https://doi.org/10.2139/ssrn.5191372.</span> +<span class="a">+Gozalo-Brizuela, Roberto, and Eduardo C. Garrido-Merchan. 2023. “ChatGPT Is Not All You</span> +<span class="a">+ Need. A State of the Art Review of Large Generative AI Models.” arXiv.</span> +<span class="a">+ https://doi.org/10.48550/arXiv.2301.04655.</span> +<span class="a">+Heyns, Emiliano. 2012. “The Ethics of Free Software,” December.</span> +<span class="a">+ https://www.academia.edu/2320407/The_Ethics_of_Free_Software.</span> +<span class="a">+James, Jeffrey. 2003. “Free Software and the Digital Divide: Opportunities and Constraints for</span> +<span class="a">+ Developing Countries.” Journal of Information Science 29 (1): 25–33.</span> +<span class="a">+ https://doi.org/10.1177/016555150302900103.</span> +<span class="a">+Liesenfeld, Andreas, and Mark Dingemanse. 2024. “Rethinking Open Source Generative AI: Open-</span> +<span class="a">+ Washing and the EU AI Act.” In Proceedings of the 2024 ACM Conference on Fairness,</span> +<span class="a">+</span> +<span class="a">+ 18</span> +<span class="a">+</span> + Accountability, and Transparency, 1774–87. FAccT ‘24. New York, NY, USA: Association for +<span class="a">+ Computing Machinery. https://doi.org/10.1145/3630106.3659005.</span> +<span class="a">+Montag, Christian, and Michèle Finck. 2024. “Successful Implementation of the EU AI Act</span> +<span class="a">+ Requires Interdisciplinary Efforts.” Nature Machine Intelligence 6 (12): 1415–17.</span> +<span class="a">+ https://doi.org/10.1038/s42256-024-00954-z.</span> +<span class="a">+Novobilská, Linda. 2023. “Free and Open Source Software Licensing Requirements and Copyright</span> +<span class="a">+ Infringement Involving Artificial Intelligence Technologies.”</span> +<span class="a">+Phillips, Ron. 2007. “Deadly Combinations: A Framework for Analyzing the GPL’s Viral Effect.”</span> +<span class="a">+ John Marshall Journal of Computer and Information Law 25:487.</span> +<span class="a">+Raymond, Eric. 1999. “The Cathedral and the Bazaar.” Knowledge, Technology & Policy 12 (3): 23–49.</span> +<span class="a">+ https://doi.org/10.1007/s12130-999-1026-0.</span> +<span class="a">+Seger, Elizabeth, Noemi Dreksler, Richard Moulange, Emily Dardaman, Jonas Schuett, K. Wei,</span> +<span class="a">+ Christoph Winter, et al. 2023. “Open-Sourcing Highly Capable Foundation Models: An</span> +<span class="a">+ Evaluation of Risks, Benefits, and Alternative Methods for Pursuing Open-Source</span> +<span class="a">+ Objectives.” arXiv. https://doi.org/10.48550/arXiv.2311.09227.</span> +<span class="a">+Shen, Celeste. 2024. “Fair Use, Licensing, and Authors’ Rights in the Age of Generative AI.”</span> +<span class="a">+ Northwestern Journal of Technology and Intellectual Property 22 (1): 157.</span> +<span class="a">+Stallman, Richard. 2002. Free Software, Free Society: Selected Essays of Richard M. Stallman. Lulu.com.</span> +<span class="a">+US Copyright Office. 2025. “Copyright and Artificial Intelligence, Part 3: Generative AI Training</span> +<span class="a">+ Pre-Publication Version,” May.</span> +<span class="a">+Wilson, Claudia, and Emmie Hine. 2025. “US Open-Source AI Governance: Balancing Ideological</span> +<span class="a">+ and Geopolitical Considerations with China Competition.” Center for AI Policy.</span> +<span class="a">+ https://cdn.prod.website-</span> +<span class="a">+ files.com/65af2088cac9fb1fb621091f/67aaca031ed677c879434284_Final_US%20Open-</span> +<span class="a">+ Source%20AI%20Governance.pdf.</span> +<span class="a">+Zajdel, Stan, Diego Elias Costa, and Hafedh Mili. 2022. “Open Source Software: An Approach to</span> +<span class="a">+ Controlling Usage and Risk in Application Ecosystems.” In Proceedings of the 26th ACM</span> +<span class="a">+ International Systems and Software Product Line Conference - Volume A, A:154–63. SPLC ‘22. New</span> +<span class="a">+ York, NY, USA: Association for Computing Machinery.</span> +<span class="a">+ https://doi.org/10.1145/3546932.3547000.</span> +<span class="a">+</span> +<span class="a">+ 19</span> +<span class="a">+</span> +</pre> +</div></body></html> diff --git a/git/CSV/commit/33fa9f2c3dd575eb28b6ac77bfc5c30fdaebbcbe.html b/git/CSV/commit/33fa9f2c3dd575eb28b6ac77bfc5c30fdaebbcbe.html new file mode 100644 index 0000000..f6b84c4 --- /dev/null +++ b/git/CSV/commit/33fa9f2c3dd575eb28b6ac77bfc5c30fdaebbcbe.html @@ -0,0 +1,37 @@ +<!DOCTYPE html> +<html lang="en"><head><meta charset="UTF-8" /> +<meta name="viewport" content="width=device-width, initial-scale=1" /> +<title>CSV 33fa9f2c - Recently Written</title> +<link rel="stylesheet" href="../../stagit.css" /> +</head><body> +<div id="top"><a href="../../../index.html">Recently Written</a> · <a href="../../index.html">git</a></div> +<h1>CSV</h1><span class="desc">Convert CSV (in a reader macro, or in a file) in to Lisp lists.</span><p class="url">git clone https://github.com/equwal/CSV</p><p><a href="../../CSV/index.html">Log</a> | <a href="../../CSV/files.html">Files</a> | <a href="../../CSV/refs.html">Refs</a></p><hr/> +<div id="content"> +<pre>commit 33fa9f2c3dd575eb28b6ac77bfc5c30fdaebbcbe +Spenser Truex <truex@equwal.com> +2026-10-05 00:02:48 -0700 + +LICENSE: add the Contextual Copyleft AI (CCAI) clause, 2026-10-04 +</pre><pre> LICENSE | 12 ++++++++++++ + 1 file changed, 12 insertions(+) +</pre><pre>diff --git a/LICENSE b/LICENSE +index f288702..7f339b1 100644 +--- a/LICENSE ++++ b/LICENSE +<span class="h">@@ -672,3 +672,15 @@ may consider it more useful to permit linking proprietary applications with</span> + the library. If this is what you want to do, use the GNU Lesser General + Public License instead of this License. But first, please read + <https://www.gnu.org/licenses/why-not-lgpl.html>. +<span class="a">+</span> +<span class="a">+Contextual Copyleft AI (CCAI) Clause</span> +<span class="a">+Added 2026-10-04.</span> +<span class="a">+If this software is used to train, fine-tune, or otherwise generate any machine learning model or generative AI</span> +<span class="a">+system, any resulting model, dataset, or system must be released under terms no more restrictive than this license.</span> +<span class="a">+This includes the requirement to make available the training code, a description of the data used in training, and</span> +<span class="a">+the model’s parameters and architecture. Providing access to such a model or its outputs via a network is</span> +<span class="a">+considered distribution and triggers this obligation.</span> +<span class="a">+</span> +<span class="a">+Paper: https://arxiv.org/pdf/2507.12713</span> +<span class="a">+Mirror: https://dickt.store/fuckai.pdf</span> +<span class="a">+Copy: ccai-2507.12713.pdf (Shanklin, Hine, Novelli, Schroder and Floridi, CC BY-NC-SA 4.0)</span></pre> +</div></body></html> diff --git a/git/CSV/file/LICENSE.html b/git/CSV/file/LICENSE.html index cf8d870..5b65b01 100644 --- a/git/CSV/file/LICENSE.html +++ b/git/CSV/file/LICENSE.html @@ -7,7 +7,7 @@ <div id="top"><a href="../../../index.html">Recently Written</a> · <a href="../../index.html">git</a></div> <h1>CSV</h1><span class="desc">Convert CSV (in a reader macro, or in a file) in to Lisp lists.</span><p class="url">git clone https://github.com/equwal/CSV</p><p><a href="../../CSV/index.html">Log</a> | <a href="../../CSV/files.html">Files</a> | <a href="../../CSV/refs.html">Refs</a></p><hr/> <div id="content"> -<p>LICENSE (35149 bytes)</p><pre class="src"><a id="l1" href="#l1">1</a> GNU GENERAL PUBLIC LICENSE +<p>LICENSE (35881 bytes)</p><pre class="src"><a id="l1" href="#l1">1</a> GNU GENERAL PUBLIC LICENSE <a id="l2" href="#l2">2</a> Version 3, 29 June 2007 <a id="l3" href="#l3">3</a> <a id="l4" href="#l4">4</a> Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/> @@ -680,5 +680,17 @@ <a id="l671" href="#l671">671</a> may consider it more useful to permit linking proprietary applications with <a id="l672" href="#l672">672</a> the library. If this is what you want to do, use the GNU Lesser General <a id="l673" href="#l673">673</a> Public License instead of this License. But first, please read -<a id="l674" href="#l674">674</a> <https://www.gnu.org/licenses/why-not-lgpl.html>.</pre> +<a id="l674" href="#l674">674</a> <https://www.gnu.org/licenses/why-not-lgpl.html>. +<a id="l675" href="#l675">675</a> +<a id="l676" href="#l676">676</a> Contextual Copyleft AI (CCAI) Clause +<a id="l677" href="#l677">677</a> Added 2026-10-04. +<a id="l678" href="#l678">678</a> If this software is used to train, fine-tune, or otherwise generate any machine learning model or generative AI +<a id="l679" href="#l679">679</a> system, any resulting model, dataset, or system must be released under terms no more restrictive than this license. +<a id="l680" href="#l680">680</a> This includes the requirement to make available the training code, a description of the data used in training, and +<a id="l681" href="#l681">681</a> the model’s parameters and architecture. Providing access to such a model or its outputs via a network is +<a id="l682" href="#l682">682</a> considered distribution and triggers this obligation. +<a id="l683" href="#l683">683</a> +<a id="l684" href="#l684">684</a> Paper: https://arxiv.org/pdf/2507.12713 +<a id="l685" href="#l685">685</a> Mirror: https://dickt.store/fuckai.pdf +<a id="l686" href="#l686">686</a> Copy: ccai-2507.12713.pdf (Shanklin, Hine, Novelli, Schroder and Floridi, CC BY-NC-SA 4.0)</pre> </div></body></html> diff --git a/git/CSV/files.html b/git/CSV/files.html index a220f0d..fd84f83 100644 --- a/git/CSV/files.html +++ b/git/CSV/files.html @@ -7,5 +7,5 @@ <div id="top"><a href="../../index.html">Recently Written</a> · <a href="../index.html">git</a></div> <h1>CSV</h1><span class="desc">Convert CSV (in a reader macro, or in a file) in to Lisp lists.</span><p class="url">git clone https://github.com/equwal/CSV</p><p><a href="../CSV/index.html">Log</a> | <a href="../CSV/files.html">Files</a> | <a href="../CSV/refs.html">Refs</a></p><hr/> <div id="content"> -<table><thead><tr><td>Mode</td><td>Name</td><td>Size</td></tr></thead><tbody><tr><td>100644</td><td><a href="file/.gitignore.html">.gitignore</a></td><td class="num">170</td></tr><tr><td>100644</td><td><a href="file/LICENSE.html">LICENSE</a></td><td class="num">35149</td></tr><tr><td>100644</td><td><a href="file/README.md.html">README.md</a></td><td class="num">57</td></tr><tr><td>100644</td><td><a href="file/csv.asd.html">csv.asd</a></td><td class="num">329</td></tr><tr><td>100644</td><td><a href="file/csv.lisp.html">csv.lisp</a></td><td class="num">2466</td></tr><tr><td>100644</td><td><a href="file/example.csv.html">example.csv</a></td><td class="num">46</td></tr><tr><td>100644</td><td><a href="file/package.lisp.html">package.lisp</a></td><td class="num">74</td></tr><tr><td>100644</td><td><a href="file/utils.lisp.html">utils.lisp</a></td><td class="num">796</td></tr></tbody></table> +<table><thead><tr><td>Mode</td><td>Name</td><td>Size</td></tr></thead><tbody><tr><td>100644</td><td><a href="file/.gitignore.html">.gitignore</a></td><td class="num">170</td></tr><tr><td>100644</td><td><a href="file/LICENSE.html">LICENSE</a></td><td class="num">35881</td></tr><tr><td>100644</td><td><a href="file/README.md.html">README.md</a></td><td class="num">57</td></tr><tr><td>100644</td><td>ccai-2507.12713.pdf <span class="dim">(not shown)</span></td><td class="num">327583</td></tr><tr><td>100644</td><td><a href="file/csv.asd.html">csv.asd</a></td><td class="num">329</td></tr><tr><td>100644</td><td><a href="file/csv.lisp.html">csv.lisp</a></td><td class="num">2466</td></tr><tr><td>100644</td><td><a href="file/example.csv.html">example.csv</a></td><td class="num">46</td></tr><tr><td>100644</td><td><a href="file/package.lisp.html">package.lisp</a></td><td class="num">74</td></tr><tr><td>100644</td><td><a href="file/utils.lisp.html">utils.lisp</a></td><td class="num">796</td></tr></tbody></table> </div></body></html> diff --git a/git/CSV/index.html b/git/CSV/index.html index 2581acd..c0fe044 100644 --- a/git/CSV/index.html +++ b/git/CSV/index.html @@ -7,5 +7,5 @@ <div id="top"><a href="../../index.html">Recently Written</a> · <a href="../index.html">git</a></div> <h1>CSV</h1><span class="desc">Convert CSV (in a reader macro, or in a file) in to Lisp lists.</span><p class="url">git clone https://github.com/equwal/CSV</p><p><a href="../CSV/index.html">Log</a> | <a href="../CSV/files.html">Files</a> | <a href="../CSV/refs.html">Refs</a></p><hr/> <div id="content"> -<table><thead><tr><td>Date</td><td>Commit message</td><td>Author</td></tr></thead><tbody><tr><td>2019-01-07 14:05</td><td><a href="commit/f122dd6eeb0eb3a971ae7b02bb3c9abc9c073c51.html">Update csv.asd</a></td><td>Spenser Truex</td></tr><tr><td>2019-01-07 13:58</td><td><a href="commit/2838a2e296e94912acb9ec834a3373fa1aa7ea38.html">Merge pull request #3 from equwal/add-license-1</a></td><td>Spenser Truex</td></tr><tr><td>2019-01-07 13:54</td><td><a href="commit/8b3dae44e341aeb4e3840914f35608c1e5c1c2de.html">Create LICENSE</a></td><td>Spenser Truex</td></tr><tr><td>2019-01-07 12:22</td><td><a href="commit/73c0ec1dfe5de713b99378928b361edc89c59ada.html">Merge pull request #2 from equwal/testing</a></td><td>Spenser Truex</td></tr><tr><td>2019-01-07 12:21</td><td><a href="commit/6deca0d81e38d24fe7cd0bc062a53c1ff4d6818c.html">Fix quoting bug and homepage on README.</a></td><td>Spenser Truex</td></tr><tr><td>2019-01-07 12:14</td><td><a href="commit/2130c21d376f5032246677667f456b1ac43018b1.html">Merge pull request #1 from equwal/testing</a></td><td>Spenser Truex</td></tr><tr><td>2019-01-07 12:07</td><td><a href="commit/bdaa1297fd38f0ad8dfab76377739f1bc08d47dd.html">typo</a></td><td>Spenser Truex</td></tr><tr><td>2019-01-07 12:07</td><td><a href="commit/19cbb6dcc701ef4714ddcd2e83f5aaaa7d34f13f.html">Homepage</a></td><td>Spenser Truex</td></tr><tr><td>2019-01-07 11:55</td><td><a href="commit/374842de191e56f2f0f5e14100f093846068aaca.html">Fix quoting bug</a></td><td>Spenser Truex</td></tr><tr><td>2019-01-07 11:52</td><td><a href="commit/4ab4cab04918986b3453b7e8c95999cfcf68134f.html">Include example.csv</a></td><td>Spenser Truex</td></tr><tr><td>2019-01-07 11:38</td><td><a href="commit/ac0bf7108272763fa16a318d8cf7bb8f53c727cc.html">Initial</a></td><td>Spenser Truex</td></tr></tbody></table> +<table><thead><tr><td>Date</td><td>Commit message</td><td>Author</td></tr></thead><tbody><tr><td>2026-10-05 00:02</td><td><a href="commit/0057099fbcfae89c4e718cac9225ce828fa221de.html">Add ccai-2507.12713.pdf: the CCAI paper, arXiv:2507.12713 (CC BY-NC-SA 4.0)</a></td><td>Spenser Truex</td></tr><tr><td>2026-10-05 00:02</td><td><a href="commit/33fa9f2c3dd575eb28b6ac77bfc5c30fdaebbcbe.html">LICENSE: add the Contextual Copyleft AI (CCAI) clause, 2026-10-04</a></td><td>Spenser Truex</td></tr><tr><td>2019-01-07 14:05</td><td><a href="commit/f122dd6eeb0eb3a971ae7b02bb3c9abc9c073c51.html">Update csv.asd</a></td><td>Spenser Truex</td></tr><tr><td>2019-01-07 13:58</td><td><a href="commit/2838a2e296e94912acb9ec834a3373fa1aa7ea38.html">Merge pull request #3 from equwal/add-license-1</a></td><td>Spenser Truex</td></tr><tr><td>2019-01-07 13:54</td><td><a href="commit/8b3dae44e341aeb4e3840914f35608c1e5c1c2de.html">Create LICENSE</a></td><td>Spenser Truex</td></tr><tr><td>2019-01-07 12:22</td><td><a href="commit/73c0ec1dfe5de713b99378928b361edc89c59ada.html">Merge pull request #2 from equwal/testing</a></td><td>Spenser Truex</td></tr><tr><td>2019-01-07 12:21</td><td><a href="commit/6deca0d81e38d24fe7cd0bc062a53c1ff4d6818c.html">Fix quoting bug and homepage on README.</a></td><td>Spenser Truex</td></tr><tr><td>2019-01-07 12:14</td><td><a href="commit/2130c21d376f5032246677667f456b1ac43018b1.html">Merge pull request #1 from equwal/testing</a></td><td>Spenser Truex</td></tr><tr><td>2019-01-07 12:07</td><td><a href="commit/bdaa1297fd38f0ad8dfab76377739f1bc08d47dd.html">typo</a></td><td>Spenser Truex</td></tr><tr><td>2019-01-07 12:07</td><td><a href="commit/19cbb6dcc701ef4714ddcd2e83f5aaaa7d34f13f.html">Homepage</a></td><td>Spenser Truex</td></tr><tr><td>2019-01-07 11:55</td><td><a href="commit/374842de191e56f2f0f5e14100f093846068aaca.html">Fix quoting bug</a></td><td>Spenser Truex</td></tr><tr><td>2019-01-07 11:52</td><td><a href="commit/4ab4cab04918986b3453b7e8c95999cfcf68134f.html">Include example.csv</a></td><td>Spenser Truex</td></tr><tr><td>2019-01-07 11:38</td><td><a href="commit/ac0bf7108272763fa16a318d8cf7bb8f53c727cc.html">Initial</a></td><td>Spenser Truex</td></tr></tbody></table> </div></body></html> diff --git a/git/CSV/refs.html b/git/CSV/refs.html index e030c43..d7bdb1a 100644 --- a/git/CSV/refs.html +++ b/git/CSV/refs.html @@ -7,5 +7,5 @@ <div id="top"><a href="../../index.html">Recently Written</a> · <a href="../index.html">git</a></div> <h1>CSV</h1><span class="desc">Convert CSV (in a reader macro, or in a file) in to Lisp lists.</span><p class="url">git clone https://github.com/equwal/CSV</p><p><a href="../CSV/index.html">Log</a> | <a href="../CSV/files.html">Files</a> | <a href="../CSV/refs.html">Refs</a></p><hr/> <div id="content"> -<table><thead><tr><td>Name</td><td>Type</td><td>Date</td><td>Commit</td></tr></thead><tbody><tr><td>add-license-1</td><td>commit</td><td>2019-01-07</td><td>8b3dae4</td></tr><tr><td>master</td><td>commit</td><td>2019-01-07</td><td>f122dd6</td></tr><tr><td>testing</td><td>commit</td><td>2019-01-07</td><td>6deca0d</td></tr></tbody></table> +<table><thead><tr><td>Name</td><td>Type</td><td>Date</td><td>Commit</td></tr></thead><tbody><tr><td>add-license-1</td><td>commit</td><td>2019-01-07</td><td>8b3dae4</td></tr><tr><td>master</td><td>commit</td><td>2026-10-05</td><td>0057099</td></tr><tr><td>testing</td><td>commit</td><td>2019-01-07</td><td>6deca0d</td></tr></tbody></table> </div></body></html> diff --git a/git/Forthe/commit/04711d93988409367a9abd7041b6fe60b92e2b36.html b/git/Forthe/commit/04711d93988409367a9abd7041b6fe60b92e2b36.html new file mode 100644 index 0000000..b81e2e4 --- /dev/null +++ b/git/Forthe/commit/04711d93988409367a9abd7041b6fe60b92e2b36.html @@ -0,0 +1,48 @@ +<!DOCTYPE html> +<html lang="en"><head><meta charset="UTF-8" /> +<meta name="viewport" content="width=device-width, initial-scale=1" /> +<title>Forthe 04711d93 - Recently Written</title> +<link rel="stylesheet" href="../../stagit.css" /> +</head><body> +<div id="top"><a href="../../../index.html">Recently Written</a> · <a href="../../index.html">git</a></div> +<h1>Forthe</h1><span class="desc">Forth-like DSL.</span><p class="url">git clone https://github.com/equwal/Forthe</p><p><a href="../../Forthe/index.html">Log</a> | <a href="../../Forthe/files.html">Files</a> | <a href="../../Forthe/refs.html">Refs</a></p><hr/> +<div id="content"> +<pre>commit 04711d93988409367a9abd7041b6fe60b92e2b36 +Spenser Truex <truex@equwal.com> +2026-10-05 00:01:55 -0700 + +LICENSE: Contextual Copyleft AI (CCAI) License v1.0 +</pre><pre> LICENSE | 25 +++++++++++++++++++++++++ + 1 file changed, 25 insertions(+) +</pre><pre>diff --git a/LICENSE b/LICENSE +new file mode 100644 +index 0000000..dea32b8 +--- /dev/null ++++ b/LICENSE +<span class="h">@@ -0,0 +1,25 @@</span> +<span class="a">+Contextual Copyleft AI (CCAI) License v1.0</span> +<span class="a">+Copyright (c) 2018-2026 Spenser Truex</span> +<span class="a">+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated</span> +<span class="a">+documentation files (the “Software”), to use, copy, modify, merge, publish, distribute, and/or sublicense copies</span> +<span class="a">+of the Software, subject to the following conditions:</span> +<span class="a">+1. Software Freedom.</span> +<span class="a">+Users are granted the freedom to run, study, modify, and distribute the Software, including modified versions,</span> +<span class="a">+for any purpose.</span> +<span class="a">+2. Copyleft Requirement.</span> +<span class="a">+Any distribution, publication, or making available of the Software — including exact copies, modified versions,</span> +<span class="a">+or derivative works — must be licensed in full under the terms of this CCAI License without imposing any</span> +<span class="a">+further restrictions. This requirement also applies to any model, dataset, or system that is trained on, derived</span> +<span class="a">+from, or incorporates the Software or its outputs. In the case of generative AI models, the obligation to apply</span> +<span class="a">+this license includes, at minimum, the disclosure of the model’s source code, a meaningful description of the</span> +<span class="a">+training data, and the model’s parameters, weights, and architecture.</span> +<span class="a">+3. Distribution Clarification.</span> +<span class="a">+Providing access to the Software or any covered model over a network or online service shall be considered</span> +<span class="a">+distribution and triggers the obligations of this License.</span> +<span class="a">+4. No Warranty.</span> +<span class="a">+The Software is provided “as is”, without warranty of any kind.</span> +<span class="a">+5. Preservation of License Notice.</span> +<span class="a">+Copies, modifications, and derivative works must retain this license notice.</span> +<span class="a">+</span> +<span class="a">+Paper: https://arxiv.org/pdf/2507.12713</span> +<span class="a">+Mirror: https://dickt.store/fuckai.pdf</span></pre> +</div></body></html> diff --git a/git/Forthe/file/LICENSE.html b/git/Forthe/file/LICENSE.html new file mode 100644 index 0000000..7e301e3 --- /dev/null +++ b/git/Forthe/file/LICENSE.html @@ -0,0 +1,35 @@ +<!DOCTYPE html> +<html lang="en"><head><meta charset="UTF-8" /> +<meta name="viewport" content="width=device-width, initial-scale=1" /> +<title>Forthe/LICENSE - Recently Written</title> +<link rel="stylesheet" href="../../stagit.css" /> +</head><body> +<div id="top"><a href="../../../index.html">Recently Written</a> · <a href="../../index.html">git</a></div> +<h1>Forthe</h1><span class="desc">Forth-like DSL.</span><p class="url">git clone https://github.com/equwal/Forthe</p><p><a href="../../Forthe/index.html">Log</a> | <a href="../../Forthe/files.html">Files</a> | <a href="../../Forthe/refs.html">Refs</a></p><hr/> +<div id="content"> +<p>LICENSE (1638 bytes)</p><pre class="src"><a id="l1" href="#l1">1</a> Contextual Copyleft AI (CCAI) License v1.0 +<a id="l2" href="#l2">2</a> Copyright (c) 2018-2026 Spenser Truex +<a id="l3" href="#l3">3</a> Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated +<a id="l4" href="#l4">4</a> documentation files (the “Software”), to use, copy, modify, merge, publish, distribute, and/or sublicense copies +<a id="l5" href="#l5">5</a> of the Software, subject to the following conditions: +<a id="l6" href="#l6">6</a> 1. Software Freedom. +<a id="l7" href="#l7">7</a> Users are granted the freedom to run, study, modify, and distribute the Software, including modified versions, +<a id="l8" href="#l8">8</a> for any purpose. +<a id="l9" href="#l9">9</a> 2. Copyleft Requirement. +<a id="l10" href="#l10">10</a> Any distribution, publication, or making available of the Software — including exact copies, modified versions, +<a id="l11" href="#l11">11</a> or derivative works — must be licensed in full under the terms of this CCAI License without imposing any +<a id="l12" href="#l12">12</a> further restrictions. This requirement also applies to any model, dataset, or system that is trained on, derived +<a id="l13" href="#l13">13</a> from, or incorporates the Software or its outputs. In the case of generative AI models, the obligation to apply +<a id="l14" href="#l14">14</a> this license includes, at minimum, the disclosure of the model’s source code, a meaningful description of the +<a id="l15" href="#l15">15</a> training data, and the model’s parameters, weights, and architecture. +<a id="l16" href="#l16">16</a> 3. Distribution Clarification. +<a id="l17" href="#l17">17</a> Providing access to the Software or any covered model over a network or online service shall be considered +<a id="l18" href="#l18">18</a> distribution and triggers the obligations of this License. +<a id="l19" href="#l19">19</a> 4. No Warranty. +<a id="l20" href="#l20">20</a> The Software is provided “as is”, without warranty of any kind. +<a id="l21" href="#l21">21</a> 5. Preservation of License Notice. +<a id="l22" href="#l22">22</a> Copies, modifications, and derivative works must retain this license notice. +<a id="l23" href="#l23">23</a> +<a id="l24" href="#l24">24</a> Paper: https://arxiv.org/pdf/2507.12713 +<a id="l25" href="#l25">25</a> Mirror: https://dickt.store/fuckai.pdf</pre> +</div></body></html> diff --git a/git/Forthe/files.html b/git/Forthe/files.html index 1d49b31..1bd76ed 100644 --- a/git/Forthe/files.html +++ b/git/Forthe/files.html @@ -7,5 +7,5 @@ <div id="top"><a href="../../index.html">Recently Written</a> · <a href="../index.html">git</a></div> <h1>Forthe</h1><span class="desc">Forth-like DSL.</span><p class="url">git clone https://github.com/equwal/Forthe</p><p><a href="../Forthe/index.html">Log</a> | <a href="../Forthe/files.html">Files</a> | <a href="../Forthe/refs.html">Refs</a></p><hr/> <div id="content"> -<table><thead><tr><td>Mode</td><td>Name</td><td>Size</td></tr></thead><tbody><tr><td>100644</td><td><a href="file/.gitignore.html">.gitignore</a></td><td class="num">25</td></tr><tr><td>100644</td><td><a href="file/README.org.html">README.org</a></td><td class="num">3869</td></tr><tr><td>100644</td><td><a href="file/forthe.el.html">forthe.el</a></td><td class="num">1557</td></tr></tbody></table> +<table><thead><tr><td>Mode</td><td>Name</td><td>Size</td></tr></thead><tbody><tr><td>100644</td><td><a href="file/.gitignore.html">.gitignore</a></td><td class="num">25</td></tr><tr><td>100644</td><td><a href="file/LICENSE.html">LICENSE</a></td><td class="num">1638</td></tr><tr><td>100644</td><td><a href="file/README.org.html">README.org</a></td><td class="num">3869</td></tr><tr><td>100644</td><td><a href="file/forthe.el.html">forthe.el</a></td><td class="num">1557</td></tr></tbody></table> </div></body></html> diff --git a/git/Forthe/index.html b/git/Forthe/index.html index ff70737..fdb7705 100644 --- a/git/Forthe/index.html +++ b/git/Forthe/index.html @@ -7,5 +7,5 @@ <div id="top"><a href="../../index.html">Recently Written</a> · <a href="../index.html">git</a></div> <h1>Forthe</h1><span class="desc">Forth-like DSL.</span><p class="url">git clone https://github.com/equwal/Forthe</p><p><a href="../Forthe/index.html">Log</a> | <a href="../Forthe/files.html">Files</a> | <a href="../Forthe/refs.html">Refs</a></p><hr/> <div id="content"> -<table><thead><tr><td>Date</td><td>Commit message</td><td>Author</td></tr></thead><tbody><tr><td>2019-06-24 12:01</td><td><a href="commit/0404a89d7d4038d12413b67b47aababbd69fbd55.html">Nominative case, yeee.</a></td><td>Spenser Truex</td></tr><tr><td>2019-06-23 23:26</td><td><a href="commit/ac28ec84a64960c8904afa45c50a3d27f4e9de88.html">No plain.</a></td><td>Spenser Truex</td></tr><tr><td>2019-06-23 23:20</td><td><a href="commit/e0c334a413d72bcdb32c0c1dbbdbbfe84e41d8ed.html">Renamed eForth (taken!) to Forthe.</a></td><td>Spenser Truex</td></tr><tr><td>2019-06-23 11:09</td><td><a href="commit/12860a6217ad7bcc00fdf69e4d90b3592f8d96dd.html">githu->github.</a></td><td>Spenser Truex</td></tr><tr><td>2019-06-23 10:07</td><td><a href="commit/fc9f405991c4c20c1e5f4b3c3ddffe7690aafcff.html">code minification: https://www.reddit.com/user/npostavs/</a></td><td>Spenser Truex</td></tr><tr><td>2019-06-23 01:00</td><td><a href="commit/574c183ffde0cbbbc8e94116315e8bd61356868a.html">== not ``.</a></td><td>Spenser Truex</td></tr><tr><td>2019-06-23 00:37</td><td><a href="commit/c92ee8daf51a5eed6f3c4ece81dda5d979af74f7.html">Made bindings let-like (no pair notation).</a></td><td>Spenser Truex</td></tr><tr><td>2019-06-22 21:05</td><td><a href="commit/ff25e9ab3f22c00225e6d185575fdcec2c49f7eb.html">Pure pandoc</a></td><td>Spenser Truex</td></tr><tr><td>2019-06-22 20:25</td><td><a href="commit/670cc87c19b74a4d5b0c955e7f8c28c94043bb5e.html">Comments in COMMENTary.</a></td><td>Spenser Truex</td></tr><tr><td>2019-06-22 20:13</td><td><a href="commit/2af5cc9aa160b85bcb45106fdf2e01b10010d99f.html">Pro-melpa implementation and boilerplate.</a></td><td>Spenser Truex</td></tr><tr><td>2019-06-22 17:42</td><td><a href="commit/9bd275f7aaed925608d727cd9781825a89551b48.html">I just threw it together</a></td><td>Spenser Truex</td></tr><tr><td>2019-06-22 17:36</td><td><a href="commit/3506f89f505ccbc3867fb6a41f9044a8af98d66b.html">new readme.</a></td><td>Spenser Truex</td></tr><tr><td>2018-12-16 11:23</td><td><a href="commit/4660cfbea38ef909abd2074af84b32bda8a9a0d8.html">labels->flet for lexical binding.</a></td><td>Spenser Truex</td></tr><tr><td>2018-12-06 13:33</td><td><a href="commit/0880bcfa3576c3a8cc2670bc6c0b0c3834c5ec7c.html">Lambda bug.</a></td><td>Spenser Truex</td></tr><tr><td>2018-12-06 13:18</td><td><a href="commit/c246bcdce5d8b5d741230e8cd2be5de48bd44c1a.html">Macro bug.</a></td><td>Spenser Truex</td></tr><tr><td>2018-12-05 20:54</td><td><a href="commit/238411ca22768e51528942867fe6ea73351fe264.html">Indent.</a></td><td>Spenser Truex</td></tr><tr><td>2018-12-05 20:53</td><td><a href="commit/eaaa7061144f21e1be9ad735051ac9240e100734.html">indent</a></td><td>Spenser Truex</td></tr><tr><td>2018-12-05 20:51</td><td><a href="commit/09f4aaaedc5a22cb1a9b74ffc67cc90cad21f473.html">indent</a></td><td>Spenser Truex</td></tr><tr><td>2018-12-05 20:49</td><td><a href="commit/89bf2b968cd774cde433fdacc1850d5baf20082f.html">indent</a></td><td>Spenser Truex</td></tr><tr><td>2018-12-05 20:48</td><td><a href="commit/fba3d8e1d627d6fb53b0212961ddb3d6fe017290.html">No html.</a></td><td>Spenser Truex</td></tr><tr><td>2018-12-05 20:47</td><td><a href="commit/b076a4347cb57a7131a771dd24f318fe7c3e01fb.html">Removed accidental scripting.</a></td><td>Spenser Truex</td></tr><tr><td>2018-12-05 20:44</td><td><a href="commit/9a9eef29928431347cecb19eef84cbd840ea7ffd.html">Bug.</a></td><td>Spenser Truex</td></tr><tr><td>2018-12-05 20:31</td><td><a href="commit/9c7443d01202ac7b38a75f00b073cdbeb0f77821.html">ENV vars supported.</a></td><td>Spenser Truex</td></tr><tr><td>2018-12-05 19:29</td><td><a href="commit/a576400ba980a182fcc44ee23182c8fa1d08b961.html">First commit.</a></td><td>Spenser Truex</td></tr></tbody></table> +<table><thead><tr><td>Date</td><td>Commit message</td><td>Author</td></tr></thead><tbody><tr><td>2026-10-05 00:01</td><td><a href="commit/04711d93988409367a9abd7041b6fe60b92e2b36.html">LICENSE: Contextual Copyleft AI (CCAI) License v1.0</a></td><td>Spenser Truex</td></tr><tr><td>2019-06-24 12:01</td><td><a href="commit/0404a89d7d4038d12413b67b47aababbd69fbd55.html">Nominative case, yeee.</a></td><td>Spenser Truex</td></tr><tr><td>2019-06-23 23:26</td><td><a href="commit/ac28ec84a64960c8904afa45c50a3d27f4e9de88.html">No plain.</a></td><td>Spenser Truex</td></tr><tr><td>2019-06-23 23:20</td><td><a href="commit/e0c334a413d72bcdb32c0c1dbbdbbfe84e41d8ed.html">Renamed eForth (taken!) to Forthe.</a></td><td>Spenser Truex</td></tr><tr><td>2019-06-23 11:09</td><td><a href="commit/12860a6217ad7bcc00fdf69e4d90b3592f8d96dd.html">githu->github.</a></td><td>Spenser Truex</td></tr><tr><td>2019-06-23 10:07</td><td><a href="commit/fc9f405991c4c20c1e5f4b3c3ddffe7690aafcff.html">code minification: https://www.reddit.com/user/npostavs/</a></td><td>Spenser Truex</td></tr><tr><td>2019-06-23 01:00</td><td><a href="commit/574c183ffde0cbbbc8e94116315e8bd61356868a.html">== not ``.</a></td><td>Spenser Truex</td></tr><tr><td>2019-06-23 00:37</td><td><a href="commit/c92ee8daf51a5eed6f3c4ece81dda5d979af74f7.html">Made bindings let-like (no pair notation).</a></td><td>Spenser Truex</td></tr><tr><td>2019-06-22 21:05</td><td><a href="commit/ff25e9ab3f22c00225e6d185575fdcec2c49f7eb.html">Pure pandoc</a></td><td>Spenser Truex</td></tr><tr><td>2019-06-22 20:25</td><td><a href="commit/670cc87c19b74a4d5b0c955e7f8c28c94043bb5e.html">Comments in COMMENTary.</a></td><td>Spenser Truex</td></tr><tr><td>2019-06-22 20:13</td><td><a href="commit/2af5cc9aa160b85bcb45106fdf2e01b10010d99f.html">Pro-melpa implementation and boilerplate.</a></td><td>Spenser Truex</td></tr><tr><td>2019-06-22 17:42</td><td><a href="commit/9bd275f7aaed925608d727cd9781825a89551b48.html">I just threw it together</a></td><td>Spenser Truex</td></tr><tr><td>2019-06-22 17:36</td><td><a href="commit/3506f89f505ccbc3867fb6a41f9044a8af98d66b.html">new readme.</a></td><td>Spenser Truex</td></tr><tr><td>2018-12-16 11:23</td><td><a href="commit/4660cfbea38ef909abd2074af84b32bda8a9a0d8.html">labels->flet for lexical binding.</a></td><td>Spenser Truex</td></tr><tr><td>2018-12-06 13:33</td><td><a href="commit/0880bcfa3576c3a8cc2670bc6c0b0c3834c5ec7c.html">Lambda bug.</a></td><td>Spenser Truex</td></tr><tr><td>2018-12-06 13:18</td><td><a href="commit/c246bcdce5d8b5d741230e8cd2be5de48bd44c1a.html">Macro bug.</a></td><td>Spenser Truex</td></tr><tr><td>2018-12-05 20:54</td><td><a href="commit/238411ca22768e51528942867fe6ea73351fe264.html">Indent.</a></td><td>Spenser Truex</td></tr><tr><td>2018-12-05 20:53</td><td><a href="commit/eaaa7061144f21e1be9ad735051ac9240e100734.html">indent</a></td><td>Spenser Truex</td></tr><tr><td>2018-12-05 20:51</td><td><a href="commit/09f4aaaedc5a22cb1a9b74ffc67cc90cad21f473.html">indent</a></td><td>Spenser Truex</td></tr><tr><td>2018-12-05 20:49</td><td><a href="commit/89bf2b968cd774cde433fdacc1850d5baf20082f.html">indent</a></td><td>Spenser Truex</td></tr><tr><td>2018-12-05 20:48</td><td><a href="commit/fba3d8e1d627d6fb53b0212961ddb3d6fe017290.html">No html.</a></td><td>Spenser Truex</td></tr><tr><td>2018-12-05 20:47</td><td><a href="commit/b076a4347cb57a7131a771dd24f318fe7c3e01fb.html">Removed accidental scripting.</a></td><td>Spenser Truex</td></tr><tr><td>2018-12-05 20:44</td><td><a href="commit/9a9eef29928431347cecb19eef84cbd840ea7ffd.html">Bug.</a></td><td>Spenser Truex</td></tr><tr><td>2018-12-05 20:31</td><td><a href="commit/9c7443d01202ac7b38a75f00b073cdbeb0f77821.html">ENV vars supported.</a></td><td>Spenser Truex</td></tr><tr><td>2018-12-05 19:29</td><td><a href="commit/a576400ba980a182fcc44ee23182c8fa1d08b961.html">First commit.</a></td><td>Spenser Truex</td></tr></tbody></table> </div></body></html> diff --git a/git/Forthe/refs.html b/git/Forthe/refs.html index b0715a2..b9a7245 100644 --- a/git/Forthe/refs.html +++ b/git/Forthe/refs.html @@ -7,5 +7,5 @@ <div id="top"><a href="../../index.html">Recently Written</a> · <a href="../index.html">git</a></div> <h1>Forthe</h1><span class="desc">Forth-like DSL.</span><p class="url">git clone https://github.com/equwal/Forthe</p><p><a href="../Forthe/index.html">Log</a> | <a href="../Forthe/files.html">Files</a> | <a href="../Forthe/refs.html">Refs</a></p><hr/> <div id="content"> -<table><thead><tr><td>Name</td><td>Type</td><td>Date</td><td>Commit</td></tr></thead><tbody><tr><td>master</td><td>commit</td><td>2019-06-24</td><td>0404a89</td></tr></tbody></table> +<table><thead><tr><td>Name</td><td>Type</td><td>Date</td><td>Commit</td></tr></thead><tbody><tr><td>master</td><td>commit</td><td>2026-10-05</td><td>04711d9</td></tr></tbody></table> </div></body></html> diff --git a/git/LispBrain/commit/054f6fc16a0c919cf53a3885f12cf06c3fe9ce65.html b/git/LispBrain/commit/054f6fc16a0c919cf53a3885f12cf06c3fe9ce65.html deleted file mode 100644 index 852b529..0000000 --- a/git/LispBrain/commit/054f6fc16a0c919cf53a3885f12cf06c3fe9ce65.html +++ /dev/null @@ -1,29 +0,0 @@ -<!DOCTYPE html> -<html lang="en"><head><meta charset="UTF-8" /> -<meta name="viewport" content="width=device-width, initial-scale=1" /> -<title>LispBrain 054f6fc1 - Recently Written</title> -<link rel="stylesheet" href="../../stagit.css" /> -</head><body> -<div id="top"><a href="../../../index.html">Recently Written</a> · <a href="../../index.html">git</a></div> -<h1>LispBrain</h1><span class="desc">The best Brainfuck debugger available, if you know how the Common Lisp debugger works. Uses Common Lisp's ease of debugging to make debugging Brainfuck code easy. Almost entirely useless.</span><p class="url">git clone https://github.com/equwal/LispBrain</p><p><a href="../../LispBrain/index.html">Log</a> | <a href="../../LispBrain/files.html">Files</a> | <a href="../../LispBrain/refs.html">Refs</a></p><hr/> -<div id="content"> -<pre>commit 054f6fc16a0c919cf53a3885f12cf06c3fe9ce65 -Spenser Truex <struex0@gmail.com> -2016-02-13 15:55:46 -0800 - -A backup for emacs was removed -</pre><pre> packages.lisp~ | 6 ------ - 1 file changed, 6 deletions(-) -</pre><pre>diff --git a/packages.lisp~ b/packages.lisp~ -deleted file mode 100644 -index 288f4ba..0000000 ---- a/packages.lisp~ -+++ /dev/null -<span class="h">@@ -1,6 +0,0 @@</span> -<span class="d">-(load "interpreter.lisp")</span> -<span class="d">-(defpackage :cl-brainfuck</span> -<span class="d">- (:use :cl-user)</span> -<span class="d">- (:export :interpret</span> -<span class="d">- :*ascii*</span> -<span class="d">- :*operators*))</span></pre> -</div></body></html> diff --git a/git/LispBrain/commit/129129c59aa4fa1391590820fb5bcb4f96afe0bd.html b/git/LispBrain/commit/129129c59aa4fa1391590820fb5bcb4f96afe0bd.html new file mode 100644 index 0000000..3dd6775 --- /dev/null +++ b/git/LispBrain/commit/129129c59aa4fa1391590820fb5bcb4f96afe0bd.html @@ -0,0 +1,37 @@ +<!DOCTYPE html> +<html lang="en"><head><meta charset="UTF-8" /> +<meta name="viewport" content="width=device-width, initial-scale=1" /> +<title>LispBrain 129129c5 - Recently Written</title> +<link rel="stylesheet" href="../../stagit.css" /> +</head><body> +<div id="top"><a href="../../../index.html">Recently Written</a> · <a href="../../index.html">git</a></div> +<h1>LispBrain</h1><span class="desc">The best Brainfuck debugger available, if you know how the Common Lisp debugger works. Uses Common Lisp's ease of debugging to make debugging Brainfuck code easy. Almost entirely useless.</span><p class="url">git clone https://github.com/equwal/LispBrain</p><p><a href="../../LispBrain/index.html">Log</a> | <a href="../../LispBrain/files.html">Files</a> | <a href="../../LispBrain/refs.html">Refs</a></p><hr/> +<div id="content"> +<pre>commit 129129c59aa4fa1391590820fb5bcb4f96afe0bd +Spenser Truex <truex@equwal.com> +2026-10-05 00:01:20 -0700 + +LICENSE: add the Contextual Copyleft AI (CCAI) clause, 2026-10-04 +</pre><pre> LICENSE | 12 ++++++++++++ + 1 file changed, 12 insertions(+) +</pre><pre>diff --git a/LICENSE b/LICENSE +index 3aa97ec..086d54a 100644 +--- a/LICENSE ++++ b/LICENSE +<span class="h">@@ -5,3 +5,15 @@ Permission is hereby granted, free of charge, to any person obtaining a copy of</span> + The above copyright notice and this permission notice shall be included in all copies or substantial portions of this Brainfuck Software. + + THE BRAINFUCK SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THIS BRAINFUCK SOFTWARE OR THE USE OR OTHER DEALINGS IN THIS BRAINFUCK SOFTWARE. +<span class="a">+</span> +<span class="a">+Contextual Copyleft AI (CCAI) Clause</span> +<span class="a">+Added 2026-10-04.</span> +<span class="a">+If this software is used to train, fine-tune, or otherwise generate any machine learning model or generative AI</span> +<span class="a">+system, any resulting model, dataset, or system must be released under terms no more restrictive than this license.</span> +<span class="a">+This includes the requirement to make available the training code, a description of the data used in training, and</span> +<span class="a">+the model’s parameters and architecture. Providing access to such a model or its outputs via a network is</span> +<span class="a">+considered distribution and triggers this obligation.</span> +<span class="a">+</span> +<span class="a">+Paper: https://arxiv.org/pdf/2507.12713</span> +<span class="a">+Mirror: https://dickt.store/fuckai.pdf</span> +<span class="a">+Copy: ccai-2507.12713.pdf (Shanklin, Hine, Novelli, Schroder and Floridi, CC BY-NC-SA 4.0)</span></pre> +</div></body></html> diff --git a/git/LispBrain/commit/5cc7a2bcf31e3ea28d09bd6533e92655f5f73d81.html b/git/LispBrain/commit/5cc7a2bcf31e3ea28d09bd6533e92655f5f73d81.html deleted file mode 100644 index 9566151..0000000 --- a/git/LispBrain/commit/5cc7a2bcf31e3ea28d09bd6533e92655f5f73d81.html +++ /dev/null @@ -1,29 +0,0 @@ -<!DOCTYPE html> -<html lang="en"><head><meta charset="UTF-8" /> -<meta name="viewport" content="width=device-width, initial-scale=1" /> -<title>LispBrain 5cc7a2bc - Recently Written</title> -<link rel="stylesheet" href="../../stagit.css" /> -</head><body> -<div id="top"><a href="../../../index.html">Recently Written</a> · <a href="../../index.html">git</a></div> -<h1>LispBrain</h1><span class="desc">The best Brainfuck debugger available, if you know how the Common Lisp debugger works. Uses Common Lisp's ease of debugging to make debugging Brainfuck code easy. Almost entirely useless.</span><p class="url">git clone https://github.com/equwal/LispBrain</p><p><a href="../../LispBrain/index.html">Log</a> | <a href="../../LispBrain/files.html">Files</a> | <a href="../../LispBrain/refs.html">Refs</a></p><hr/> -<div id="content"> -<pre>commit 5cc7a2bcf31e3ea28d09bd6533e92655f5f73d81 -Spenser Truex <struex0@gmail.com> -2016-02-13 15:54:06 -0800 - -Ignored some files - -</pre><pre> .gitignore | 4 ++++ - 1 file changed, 4 insertions(+) -</pre><pre>diff --git a/.gitignore b/.gitignore -new file mode 100644 -index 0000000..db685ce ---- /dev/null -+++ b/.gitignore -<span class="h">@@ -0,0 +1,4 @@</span> -<span class="a">+interpreter.fasl</span> -<span class="a">+interpreter.fasl~</span> -<span class="a">+packages.fasl</span> -<span class="a">+packages.fasl~</span> -\ No newline at end of file</pre> -</div></body></html> diff --git a/git/LispBrain/commit/61bbd5e7d6a5b8da90ef6ca181a4807c2db0b6b4.html b/git/LispBrain/commit/61bbd5e7d6a5b8da90ef6ca181a4807c2db0b6b4.html new file mode 100644 index 0000000..fac10e4 --- /dev/null +++ b/git/LispBrain/commit/61bbd5e7d6a5b8da90ef6ca181a4807c2db0b6b4.html @@ -0,0 +1,735 @@ +<!DOCTYPE html> +<html lang="en"><head><meta charset="UTF-8" /> +<meta name="viewport" content="width=device-width, initial-scale=1" /> +<title>LispBrain 61bbd5e7 - Recently Written</title> +<link rel="stylesheet" href="../../stagit.css" /> +</head><body> +<div id="top"><a href="../../../index.html">Recently Written</a> · <a href="../../index.html">git</a></div> +<h1>LispBrain</h1><span class="desc">The best Brainfuck debugger available, if you know how the Common Lisp debugger works. Uses Common Lisp's ease of debugging to make debugging Brainfuck code easy. Almost entirely useless.</span><p class="url">git clone https://github.com/equwal/LispBrain</p><p><a href="../../LispBrain/index.html">Log</a> | <a href="../../LispBrain/files.html">Files</a> | <a href="../../LispBrain/refs.html">Refs</a></p><hr/> +<div id="content"> +<pre>commit 61bbd5e7d6a5b8da90ef6ca181a4807c2db0b6b4 +Spenser Truex <truex@equwal.com> +2026-10-05 00:01:21 -0700 + +Add ccai-2507.12713.pdf: the CCAI paper, arXiv:2507.12713 (CC BY-NC-SA 4.0) +</pre><pre> ccai-2507.12713.pdf | Bin 0 -> 327583 bytes + 1 file changed, 0 insertions(+), 0 deletions(-) +</pre><pre>diff --git a/ccai-2507.12713.pdf b/ccai-2507.12713.pdf +new file mode 100644 +index 0000000..7d50d46 +--- /dev/null ++++ b/ccai-2507.12713.pdf +<span class="h">@@ -0,0 +1,693 @@</span> +<span class="a">+The Case for Contextual Copyleft: Licensing Open Source Training Data and Generative AI</span> +<span class="a">+</span> +<span class="a">+Grant Shanklin1,2, Emmie Hine1,3,4, Claudio Novelli1, Tyler Schroder1,2, Luciano Floridi1,3</span> +<span class="a">+</span> +<span class="a">+1 Digital Ethics Center, Yale University, 85 Trumbull St, New Haven, CT 06511</span> +<span class="a">+2 Department of Computer Science, Yale University, 51 Prospect St, New Haven, CT 06511</span> +<span class="a">+3 Department of Legal Studies, University of Bologna, Via Zamboni 27/29, 40126 Bologna, Italy</span> +<span class="a">+4 Center for IT & IP Law, KU Leuven, Sint-Michielsstraat, 6 30000 Leuven, BE</span> +<span class="a">+</span> +<span class="a">+Email for correspondence: grant.shanklin@yale.edu</span> +<span class="a">+</span> +<span class="a">+Abstract</span> +<span class="a">+The proliferation of generative AI systems has created new challenges for the Free and Open Source</span> +<span class="a">+Software (FOSS) community, particularly regarding how traditional copyleft principles should apply</span> +<span class="a">+when open source code is used to train AI models. This article introduces the Contextual Copyleft AI</span> +<span class="a">+(CCAI) license, a novel licensing mechanism that extends copyleft requirements from training data to</span> +<span class="a">+the resulting generative AI models. The CCAI license offers significant advantages, including</span> +<span class="a">+enhanced developer control, incentivization of open source AI development, and mitigation of open-</span> +<span class="a">+washing practices. This is demonstrated through a structured three-part evaluation framework that</span> +<span class="a">+examines (1) legal feasibility under current copyright law, (2) policy justification comparing traditional</span> +<span class="a">+software and AI contexts, and (3) synthesis of cross-contextual benefits and risks. However, the</span> +<span class="a">+increased risk profile of open source AI, particularly the potential for direct misuse, necessitates</span> +<span class="a">+complementary regulatory approaches to achieve an appropriate risk-benefit balance. The paper</span> +<span class="a">+concludes that when implemented within a robust regulatory environment focused on responsible AI</span> +<span class="a">+usage, the CCAI license provides a viable mechanism for preserving and adapting core FOSS</span> +<span class="a">+principles to the evolving landscape of generative AI development.</span> +<span class="a">+</span> +<span class="a">+Keywords</span> +<span class="a">+Copyleft, Open source, Free Software, Generative AI, Responsible AI</span> +<span class="a">+</span> +<span class="a">+ 1</span> +<span class="a">+</span> +1. Introduction +<span class="a">+With the recent increase in the number (Gozalo-Brizuela and Garrido-Merchan 2023) and power</span> +<span class="a">+(Bandi, Adapa, and Kuchi 2023) of generative AI systems, the open source community faces new</span> +<span class="a">+challenges in responding effectively. There is considerable discussion regarding the adoption of open</span> +<span class="a">+source generative AI models, particularly given the substantial opportunities and risks that accompany</span> +<span class="a">+making these systems open (Eiras, Petrov, Vidgen, Schroeder, et al. 2024; Floridi et al. 2025). Some</span> +<span class="a">+participants in the debate suggest shifting the focus away from open source generative AI, emphasizing</span> +<span class="a">+the need for responsible AI through behavioral use licensing instead (Contractor et al. 2022).</span> +<span class="a">+Questions persist about how the open source community should manage models trained on open</span> +<span class="a">+source code (a practice notably illustrated by GitHub’s CoPilot, as discussed in Section 4.2). This</span> +<span class="a">+dialogue focuses on preserving and adapting the rules, principles, and practices of the Free and Open</span> +<span class="a">+Source Software (FOSS) movement to navigate the evolving technical landscape introduced by</span> +<span class="a">+generative AI.</span> +<span class="a">+</span> +<span class="a">+ One way the FOSS movement has historically protected free software is through “copyleft”</span> +<span class="a">+licenses. These licenses require that derivative works be distributed under the same open source license</span> +<span class="a">+as the original work. This safeguards free and open source software because copyleft propagates</span> +<span class="a">+licenses from source material to derivative works, creating a viral effect where both original and</span> +<span class="a">+derivative works remain free and open (Phillips 2007). If attributes such as “freedom” and “openness”</span> +<span class="a">+are desirable for generative AI systems, then copyleft may provide a promising mechanism for the</span> +<span class="a">+FOSS community to uphold traditional software freedoms and extend their normative commitments</span> +<span class="a">+into the emerging field of generative AI.</span> +<span class="a">+</span> +<span class="a">+ This paper examines whether such an extension of copyleft principles is desirable and explores</span> +<span class="a">+potential mechanisms for achieving it. The Contextual Copyleft AI (CCAI) license is proposed, which may</span> +<span class="a">+cover both traditional software and generative AI contexts. Then, the challenges of evaluating licenses</span> +<span class="a">+that cross contextual boundaries are discussed, considering the legal and policy considerations in each</span> +<span class="a">+context. This leads to an argument about how such a license should be evaluated. Next, this structured</span> +<span class="a">+evaluation is completed for the CCAI license. The analysis concludes that the license could be</span> +<span class="a">+beneficial, particularly when it is used within a strong regulatory environment. The license provides</span> +<span class="a">+developers with greater control over how their code is used, encourages the development of open</span> +<span class="a">+source generative AI systems, and addresses concerns related to openwashing. Regulating the</span> +<span class="a">+responsible use of AI would address the risk-benefit imbalance inherent in copyleft, as highlighted by</span> +<span class="a">+the evaluation. This license highlights emerging cases where carefully constructed and thoughtfully</span> +<span class="a">+</span> +<span class="a">+ 2</span> +<span class="a">+</span> +evaluated copyleft licenses cross contextual boundaries, offering essential insights into how to preserve +<span class="a">+and adapt free and open source norms, principles, and practices in a changing technological landscape.</span> +<span class="a">+</span> +<span class="a">+2. The Contextual Copyleft AI (CCAI) License</span> +<span class="a">+This section proposes a new open source license that harnesses the features of copyleft to enable</span> +<span class="a">+developers to allow only open source generative AI models to train on their code. The copyleft scheme</span> +<span class="a">+enables copyright holders to permit users of their software to use, modify, and redistribute their code</span> +<span class="a">+in any way, provided that the modified or redistributed version is licensed under the same terms as</span> +<span class="a">+the original work (Stallman 2002). Fundamentally, copyleft is a mechanism that leverages the existing</span> +<span class="a">+copyright system to protect and promote the freedom of users to run, copy, distribute, study, change,</span> +<span class="a">+and improve software.</span> +<span class="a">+</span> +<span class="a">+ We will refer to the proposed license as the Contextual Copyleft AI (CCAI) license, a name that</span> +<span class="a">+draws on this license’s ability to cross technical and legal contexts. This ability necessitates a nuanced</span> +<span class="a">+evaluation of this license in the sections that follow. The full license text and a clause that can be added</span> +<span class="a">+to existing open source licenses are included in the appendix of this paper. The CCAI license consists</span> +<span class="a">+of three main components:</span> +<span class="a">+</span> +<span class="a">+ 1. Software freedom: Users can run the software, modify the code to fit their needs, and</span> +<span class="a">+ distribute copies of the software.</span> +<span class="a">+</span> +<span class="a">+ 2. Copyleft requirement: Any verbatim copy, modified version, derivative work, or model trained</span> +<span class="a">+ on this code must also be wholly licensed under CCAI with no additional restrictions. This</span> +<span class="a">+ includes a description of the training data, the code used to train and run the model, and the</span> +<span class="a">+ model’s parameters for generative AI models.1</span> +<span class="a">+</span> +<span class="a">+ 3. Distribution clarification: Any distributed CCAI software must include source code or provide</span> +<span class="a">+ a way to access it. Providing access to software over the network is considered equivalent to</span> +<span class="a">+ distributing the software.</span> +<span class="a">+</span> +<span class="a">+The CCAI license incorporates a copyleft, share-alike clause to propagate open source requirements</span> +<span class="a">+from training data to the AI model itself. It adheres to the open source norm of imposing terms on</span> +<span class="a">+distribution without using code to protect software freedom via an extended definition of “distribute.”</span> +<span class="a">+Furthermore, it enhances existing open source copyleft licenses by explicitly defining terms related to</span> +<span class="a">+</span> +<span class="a">+1 The description of the training data is inspired by the Open Source Initiative’s “Open Source AI” definition found at</span> +<span class="a">+https://opensource.org/ai/open-source-ai-definition. Description should generally include what data was used to train</span> +<span class="a">+the model, where the data originated, how it was selected and obtained, and discussion of any training or fliting methods.</span> +<span class="a">+</span> +<span class="a">+ 3</span> +<span class="a">+</span> +generative AI, such as including non-code aspects of an AI model in the definition of “entire work,” +<span class="a">+which helps enable developers to freely share their code online while preventing the unconstrained</span> +<span class="a">+training of AI models with their work. This is based on the premise that using code to train generative</span> +<span class="a">+AI models is not considered fair use, as discussed above. Note that adding the CCAI clause to an</span> +<span class="a">+existing open-source license effectively creates a new variant of the license. Care should be taken to</span> +<span class="a">+ensure compatibility with other open-source licenses, as some existing licenses (such as the GPL) do</span> +<span class="a">+not permit additional restrictions beyond their stated terms.</span> +<span class="a">+</span> +<span class="a">+ The first component of the license is software freedom, defined as allowing users to “run the</span> +<span class="a">+software, modify the code to fit their needs, and distribute copies of the software.” This establishes</span> +<span class="a">+the license’s goal and ensures that the usage rights of the code are granted to the software user</span> +<span class="a">+unconditionally, in accordance with all other open source licenses. This provision is essential for</span> +<span class="a">+making the license open source and supporting all four software freedoms.</span> +<span class="a">+</span> +<span class="a">+ The second component of the license is the copyleft requirement, which mandates that “any</span> +<span class="a">+verbatim copy, modified version, derivative work, or model trained on this code must also be wholly</span> +<span class="a">+licensed under CCAI, with no additional restrictions.” This requirement closely resembles the copyleft</span> +<span class="a">+stipulations in the GNU General Public License (GPL) and Affero General Public License (AGPL)</span> +<span class="a">+licenses,2 with the added categorization of AI models as part of derivative works. This definition would</span> +<span class="a">+impose share-alike terms on AI models if using code for training is not deemed fair use (if such training</span> +<span class="a">+were fair use, the original copyright holder’s license conditions could not apply to the model).</span> +<span class="a">+However, while the courts have yet to determine whether this training constitutes fair use, it is crucial</span> +<span class="a">+to include this clarification. The most significant addition to the fundamentals of the GPL and AGPL</span> +<span class="a">+is the second part of this component, which outlines the specific elements that must be licensed under</span> +<span class="a">+CCAI, particularly for AI models. The open sourcing must encompass “a description of the training</span> +<span class="a">+data, the code used to train and run the model, and the model’s parameters” under the same license</span> +<span class="a">+without additional restrictions. This definition was based on the Open Source Initiative’s definition of</span> +<span class="a">+Open Source AI.3 This definition helps combat open-washing, because it specifies the minimum</span> +<span class="a">+threshold for what counts as an open source model in the eyes of a copyleft license.</span> +<span class="a">+</span> +<span class="a">+ The third component of the license is the distribution clarification, a clause taken from the</span> +<span class="a">+AGPL license that categorizes accessing software over the network as distributing the software being</span> +<span class="a">+accessed. This clarification is essential to ensure that model users’ access via the internet is considered</span> +<span class="a">+</span> +<span class="a">+2 These are two popular OSS licenses, with AGPL targeted towards network-facing software.</span> +<span class="a">+3 https://opensource.org/ai/open-source-ai-definition</span> +<span class="a">+</span> +<span class="a">+ 4</span> +<span class="a">+</span> +“distributed” rather than being used by the developer. Otherwise, a model trained on data licensed +<span class="a">+under CCAI could allow users to access it through a website without requiring the model to be open</span> +<span class="a">+source, as these obligations come into effect upon distribution.</span> +<span class="a">+</span> +<span class="a">+3. Evaluating Licenses Propagating Between Contexts</span> +<span class="a">+Licenses that are designed to propagate between works in different technical and legal contexts pose</span> +<span class="a">+unique challenges in evaluating their legal and policy implications. This section argues that the</span> +<span class="a">+changing context of such licenses necessitates three components. First, a legal analysis is conducted</span> +<span class="a">+to determine whether the propagation mechanism is permissible within the existing legal framework.</span> +<span class="a">+Second, a policy analysis is performed to assess the individual benefits of copyleft within each context.</span> +<span class="a">+This analysis must then be synthesized to determine the overall desirability of such licenses. While the</span> +<span class="a">+necessity of the legal analysis is straightforward, the policy consideration is more nuanced. An open</span> +<span class="a">+source license may be desirable in its original context and be able to propagate to another context, but</span> +<span class="a">+the license may not be desirable in that new context. There are additional considerations, as the</span> +<span class="a">+primary justification for copyleft licenses is necessarily context-dependent; therefore, the rationale for</span> +<span class="a">+a license will change as it propagates between contexts. After introducing this framework for</span> +<span class="a">+evaluating licenses that propagate across different technical and legal contexts, it will be applied to the</span> +<span class="a">+CCAI license proposed above.</span> +<span class="a">+</span> +<span class="a">+ Technical contexts refer to sets of software that encompass work with similar risks of being</span> +<span class="a">+made open source. For instance, traditional software refers to software that is commonly open sourced</span> +<span class="a">+today, including desktop applications, computer science tools, and server software. The risks</span> +<span class="a">+associated with traditional open source software may include security vulnerabilities, dependency</span> +<span class="a">+management challenges, and concerns regarding project sustainability (Zajdel, Costa, and Mili, 2022).</span> +<span class="a">+Another context could be generative AI because the risks of releasing a generative AI model as open</span> +<span class="a">+source would be fundamentally different; the risks are focused on the harm that could be caused either</span> +<span class="a">+by humans using the model or by a misaligned model itself (Amodei et al. 2016). These technical</span> +<span class="a">+contexts may or may not exist within the same legal contexts. Typically, technologies operating in</span> +<span class="a">+various technical contexts are governed by the same overarching legal framework, although specific</span> +<span class="a">+regulatory requirements may differ.</span> +<span class="a">+</span> +<span class="a">+ The first component in evaluating a license designed to propagate between works in different</span> +<span class="a">+contexts is a legal evaluation of the feasibility of the propagation mechanism. While the necessity of</span> +<span class="a">+such analysis is self-evident, the examination itself may prove complex. This evaluation must primarily</span> +<span class="a">+</span> +<span class="a">+ 5</span> +<span class="a">+</span> +establish two elements: first, that the work to which the license propagates qualifies as a derivative of +<span class="a">+the original work under applicable copyright law; and second, that the incorporation of the original</span> +<span class="a">+work within the derivative work falls outside the scope of the fair use doctrine. If these conditions are</span> +<span class="a">+satisfied, it can generally be concluded that the license governing the original work may impose legal</span> +<span class="a">+obligations on its use within the derivative work, including the enforcement of share-alike provisions.</span> +<span class="a">+</span> +<span class="a">+ The second component is a policy evaluation of the use of copyleft in each context</span> +<span class="a">+individually. This must be done in each context individually because different technical contexts</span> +<span class="a">+present fundamentally different risk profiles that affect the cost-benefit analysis underlying the</span> +<span class="a">+justification of free software. These varying risks mean that the same copyleft license may be justified</span> +<span class="a">+in one context but unjustified in another, even when applied to functionally related software. The</span> +<span class="a">+conclusions from the separate policy evaluations must be considered jointly in determining whether</span> +<span class="a">+such a license should be used.</span> +<span class="a">+</span> +<span class="a">+ Copyleft licenses are politically justified by their ability to protect and promote free and open</span> +<span class="a">+source software.4 Thus, the justification for using copyleft within a specific context depends on the</span> +<span class="a">+rationale for using free and open source software in that context. If the use of free software generally</span> +<span class="a">+can’t be shown to be beneficial in a technical and legal context, then copyleft can’t be shown to be</span> +<span class="a">+beneficial either. Furthermore, the justification of free software must also consider the risks associated</span> +<span class="a">+with making a piece of software open source. Thus, when technical contexts change, the specifics of</span> +<span class="a">+the justification for free software, and therefore the justification for using copyleft, must change as</span> +<span class="a">+well. The overarching argument in support of free software may remain unchanged; however, the</span> +<span class="a">+specifics of the argument must evolve either way. For instance, copyleft on a traditional piece of</span> +<span class="a">+software may be justified by a pragmatic argument regarding the quality of software produced by free</span> +<span class="a">+and open source communities. If this license propagates to a generative AI system, the justification</span> +<span class="a">+may or may not change, remaining pragmatic or taking on moral terms instead. However, the specifics</span> +<span class="a">+of the argument must change due to the different fundamental risks associated with making the</span> +<span class="a">+software open source. Therefore, the policy evaluation must be conducted individually for each</span> +<span class="a">+technical context.</span> +<span class="a">+</span> +<span class="a">+ The third component is a policy analysis of copyleft propagation as a whole. While the policy</span> +<span class="a">+analysis justifying the use of copyleft and open source must be conducted individually in each context,</span> +<span class="a">+the results should be considered collectively to make a final determination as to whether the license</span> +<span class="a">+</span> +<span class="a">+4 https://www.gnu.org/licenses/licenses.html</span> +<span class="a">+</span> +<span class="a">+ 6</span> +<span class="a">+</span> +should be used or not. This synthesis must evaluate the specific benefits and drawbacks of the +<span class="a">+propagation mechanism itself, including: whether it empowers developers by giving them appropriate</span> +<span class="a">+control over how their work is used across contexts; whether it creates positive incentives for open</span> +<span class="a">+source development in the new context; whether it helps maintain the integrity of open source</span> +<span class="a">+principles as they extend to new technologies; and whether these benefits of cross-context propagation</span> +<span class="a">+outweigh any tensions created by applying the same license across different risk profiles. The synthesis</span> +<span class="a">+should conclude with a holistic assessment of whether the propagation serves the overarching goals</span> +<span class="a">+of the FOSS community.</span> +<span class="a">+</span> +<span class="a">+4. Contextual Copyleft from Traditional Software to Generative AI</span> +<span class="a">+In this section, we evaluate the proposed CCIA license using the three-part analysis described in the</span> +<span class="a">+previous section. First, we examine the legal mechanisms through which copyleft licenses could extend</span> +<span class="a">+from training data to AI models, focusing on the critical question of whether AI training constitutes</span> +<span class="a">+fair use under US copyright law. This legal analysis proceeds under the assumption that such training</span> +<span class="a">+does not automatically qualify as fair use, allowing us to explore how licensing terms might apply.</span> +<span class="a">+</span> +<span class="a">+ Second, we evaluate the use of copyleft licenses in each context individually—traditional</span> +<span class="a">+software and generative AI—to understand how the justification for these licenses changes across</span> +<span class="a">+contexts. This comparative analysis reveals significant differences in the risk profiles between</span> +<span class="a">+traditional open source software and open source AI, particularly regarding the potential for direct</span> +<span class="a">+misuse and harm generation.</span> +<span class="a">+</span> +<span class="a">+ Finally, we synthesize these analyses to demonstrate that a copyleft license that propagates to</span> +<span class="a">+AI systems offers three key benefits: it grants developers greater agency over how their code is used</span> +<span class="a">+in AI development, stimulates the creation of open source generative AI systems, and combats “open-</span> +<span class="a">+washing” practices. Practical enforcement of the CCAI license against AI models trained on publicly</span> +<span class="a">+available data might be challenging due to difficulties in detecting unauthorized usage. However, even</span> +<span class="a">+in cases where enforcement is challenging, the existence of clearly articulated license terms can serve</span> +<span class="a">+as an effective deterrent against non-compliant use by law-abiding entities. We also acknowledge that</span> +<span class="a">+the increased risks associated with open source AI, particularly the potential for direct misuse, require</span> +<span class="a">+consideration of complementary regulatory approaches. We conclude by arguing that responsible AI</span> +<span class="a">+regulation can work alongside copyleft licensing to better align the risks and benefits of free software</span> +<span class="a">+principles in the context of generative AI.</span> +<span class="a">+</span> +<span class="a">+ 7</span> +<span class="a">+</span> +4.1. Legal Analysis +<span class="a">+Consider the case of a developer trying to prevent their publicly published work from being used to</span> +<span class="a">+train generative AI models. What legal tools are available to this person, and under what circumstances</span> +<span class="a">+can they be used?</span> +<span class="a">+</span> +<span class="a">+ Under United States (US) copyright law, authors of original works receive several exclusive</span> +<span class="a">+rights, codified in 17 U.S.C. § 106. These include the rights to reproduce the work, prepare derivative</span> +<span class="a">+works based on it, distribute copies to the public, and publicly perform or display the work. However,</span> +<span class="a">+these rights are subject to statutory limitations, including exceptions such as fair use and compulsory</span> +<span class="a">+licensing schemes that serve broader social and economic interests. Copyright automatically applies to</span> +<span class="a">+original works when they are fixed in a tangible medium, including digital formats such as online</span> +<span class="a">+content. This means that any original code published online is immediately protected. Licenses specify</span> +<span class="a">+the rights that are legally reserved for the copyright holder and grant other rights to users of the work,</span> +<span class="a">+often with attached terms and conditions. In the case of copyleft, this arrangement is reversed via a</span> +<span class="a">+license, with all rights granted to the user on the condition that derivative works are released under</span> +<span class="a">+the same license.</span> +<span class="a">+</span> +<span class="a">+ Fair use is the primary limitation of licenses serving as a method to grant developers greater</span> +<span class="a">+control over how their code is used. Licenses, as contracts between the copyright owner and the</span> +<span class="a">+software user, can only place terms on rights reserved for the copyright holder. Under US copyright</span> +<span class="a">+law, the doctrine of fair use, determined by a set of four standards, allows an existing work to be used</span> +<span class="a">+to create a new work such that it is no longer considered a derivative work of the original if the new</span> +<span class="a">+work is sufficiently “transformative.” Therefore, the original copyright holder cannot impose</span> +<span class="a">+conditions on how their work is used if the new work qualifies as fair use.</span> +<span class="a">+</span> +<span class="a">+ Regarding generative AI, if it becomes established through the courts or legislation that using</span> +<span class="a">+code to train a model is always fair use of that training data, the discussion of licenses in this context</span> +<span class="a">+becomes irrelevant. Licenses would be unable to restrict what models can train on, given that publicly</span> +<span class="a">+available code is unrestricted. However, there is uncertainty about whether training models on code</span> +<span class="a">+is, or should be, regarded as fair use (Charlesworth 2024). For instance, some argue that the</span> +<span class="a">+substantially similar relationship between the input and output of a model, the commercial purpose</span> +<span class="a">+of many AI, models, and how models can replace the source material in the market suggest that</span> +<span class="a">+training on copyrighted material may not qualify as fair use (Shen 2024).</span> +<span class="a">+</span> +<span class="a">+ The May 2025 US Copyright Office report provides crucial (although non-binding) guidance</span> +<span class="a">+on this uncertainty. The report concludes that training AI models on copyrighted works “clearly</span> +<span class="a">+</span> +<span class="a">+ 8</span> +<span class="a">+</span> +implicates the right of reproduction,” explicitly rejecting any blanket fair use protection for AI training +<span class="a">+(US Copyright Office 2025). The Office emphasized that the reproduction right is triggered when</span> +<span class="a">+copyrighted works are copied into training datasets, regardless of whether those copies are retained</span> +<span class="a">+after the training process is complete. This finding directly challenges the argument that AI training</span> +<span class="a">+constitutes non-expressive use, exempt from copyright concerns. The report further notes that</span> +<span class="a">+commercial uses creating competing content are less likely to qualify as fair use, while research and</span> +<span class="a">+non-commercial uses receive more favorable treatment—a distinction particularly relevant for</span> +<span class="a">+commercial code generation models.</span> +<span class="a">+</span> +<span class="a">+ With the assumption that this sort of training is not fair use, we can hypothesize that</span> +<span class="a">+developers can use licenses to restrict how their code is used to train generative AI models. In this</span> +<span class="a">+case, generative AI models trained on code could plausibly be considered derivative works of the</span> +<span class="a">+training data, although this interpretation remains legally unsettled, as the exact code from the training</span> +<span class="a">+data is not present in the model’s codebase and is never technically executed by the model. A similar</span> +<span class="a">+conclusion as to the ability of open source licenses to propagate to AI models has been found where</span> +<span class="a">+Benhamou argues “when the AI model incorporates open code into its program or open data into its</span> +<span class="a">+training dataset, there is a risk of propagation, i.e. a risk that the entire model or the input data become</span> +<span class="a">+fully open, as the AI model or the input can be considered as derivative of the open code or data,</span> +<span class="a">+whose license requires derivatives to remain open” (Benhamou 2024).</span> +<span class="a">+</span> +<span class="a">+ The uncertainty surrounding whether the use of open source code as training data constitutes</span> +<span class="a">+fair use warrants consideration in its own right. The current confusion in the legal landscape naturally</span> +<span class="a">+leads developers to restrict licenses defensively, potentially hindering both AI innovation and open</span> +<span class="a">+source collaboration. A clear, purpose-built license can overcome this confusion by providing explicit</span> +<span class="a">+guidance that operates effectively under multiple legal scenarios. If training is not deemed fair use,</span> +<span class="a">+then licenses become the primary mechanism for controlling the use of AI training. A license</span> +<span class="a">+specifically addressing AI training for code generation models would provide clear guidance to all</span> +<span class="a">+parties, potentially increasing the pool of code available for training while respecting developer</span> +<span class="a">+preferences. However, if courts ultimately determine that training AI models constitutes fair use, such</span> +<span class="a">+licensing terms would have no legal force. And it is worth noting that jurisdictions outside the US,</span> +<span class="a">+such as the EU, provide explicit text and data mining (TDM) exceptions, which permit data mining</span> +<span class="a">+unless right-holders explicitly opt-out through licensing terms. Thus, the CCAI license terms could</span> +<span class="a">+effectively serve as an explicit opt-out of such statutory exceptions under EU law. Therefore, we see</span> +<span class="a">+</span> +<span class="a">+ 9</span> +<span class="a">+</span> +legal value in such a license, regardless of how the US courts ultimately rule on the question of fair +<span class="a">+use.</span> +<span class="a">+</span> +<span class="a">+4.2. Policy Analysis in Each Context</span> +<span class="a">+The FOSS community generally agrees on the benefits of FOSS, which, according to the community,</span> +<span class="a">+include reliability, security, and low cost (AlMarzouq et al. 2005). However, sub-movements of the</span> +<span class="a">+community disagree on the intellectual justification for FOSS. The free software (FS) movement</span> +<span class="a">+presents moral claims to advocate for free software as defined above. Then, there is the open source</span> +<span class="a">+(OS) movement, which offers pragmatic arguments regarding the value of free software. The</span> +<span class="a">+differences can be summed up as “open source is a development methodology; free software is a</span> +<span class="a">+social movement,” but their “practical recommendations” are largely similar (Stallman 2002).</span> +<span class="a">+</span> +<span class="a">+ The FS movement argues for free software based on what Richard Stallman describes as the</span> +<span class="a">+“Golden Rule” applied to software development. He argues that proprietary software is destructive</span> +<span class="a">+insofar as it “reduces the amount and the ways that a program can be used” and, as a result, reduces</span> +<span class="a">+the wealth humanity derives from the program (Stallman 2002). He ties this to Kantian ethics, arguing</span> +<span class="a">+that a good citizen does not use destructive means, such as proprietary software, to become wealthier.</span> +<span class="a">+With this, he concludes that “programmers have the duty to encourage others to share, redistribute,</span> +<span class="a">+study, and improve the software we write” (Stallman 2002). Another way that this has been formulated</span> +<span class="a">+is to start from the premise that software has permeated society, and, under a proprietary model,</span> +<span class="a">+software producers can control the availability of and access to software. As a result, “we are beholden</span> +<span class="a">+to these producers to run our modern society and are theoretically at their whim” (Heyns 2012).</span> +<span class="a">+Proponents of FS argue that this outcome is undesirable. Instead, software should be regarded as</span> +<span class="a">+infrastructure, as it is essential for societal participation and engagement. Thus, the assertion is made</span> +<span class="a">+that society should regulate access to the software “infrastructure” rather than software companies.</span> +<span class="a">+This approach helps prevent what has been termed “the digital divide,” wherein select groups of</span> +<span class="a">+people have access to the broad digital ecosystem (James 2003).</span> +<span class="a">+</span> +<span class="a">+ The OS movement advocates for free software from a strictly pragmatic or utilitarian</span> +<span class="a">+framework, arguing that free software is more beneficial to society than proprietary software. Software</span> +<span class="a">+plays a critical role today, making society interested in the quality and reliability of the software that is</span> +<span class="a">+produced and used. OS advocates argue that making source code freely available online for public</span> +<span class="a">+collaboration results in high-quality and highly reliable software. The OS community generally posits</span> +<span class="a">+that this is due to the ability to peer review software and provide more timely fixes because more</span> +<span class="a">+</span> +<span class="a">+ 10</span> +<span class="a">+</span> +developers can review source code in search of bugs and security risks. When these issues are +<span class="a">+identified, a distributed community can work together to create solutions, rather than a select group</span> +<span class="a">+within a specific company addressing the problem. The idea that a distributed community can lead to</span> +<span class="a">+better software outcomes is summed up in the famous Linus’s Law: “Given enough eyeballs, all bugs</span> +<span class="a">+are shallow” (Raymond 1999). OS is distinct from FS in that it does not make moral claims, but is</span> +<span class="a">+based on pragmatic claims regarding which development model will yield the highest quality and most</span> +<span class="a">+reliable software.</span> +<span class="a">+</span> +<span class="a">+ The primary risks of releasing certain pieces of traditional software as open source include</span> +<span class="a">+security vulnerabilities and software misuse (Zajdel, Costa, and Mili 2022). Regarding security</span> +<span class="a">+vulnerabilities, the risk of malicious actors discovering and exploiting these vulnerabilities can be</span> +<span class="a">+mitigated by allowing multiple developers to review the code and identify and fix bugs. Thus, “open</span> +<span class="a">+source does not pose any significant barriers to security but rather reinforces sound security practices</span> +<span class="a">+by involving many people that expose bugs quickly and offers side-effects that provide customers and</span> +<span class="a">+the community with concrete examples of reusable, secure, and working code” (Clarke, Dorwin, and</span> +<span class="a">+Nash 2009).</span> +<span class="a">+</span> +<span class="a">+ Moving from the traditional software context to generative AI, the risk profile changes. The</span> +<span class="a">+security risks of open source AI are non-negligible, with a survey revealing that 10% of IT decision-</span> +<span class="a">+makers have accidentally installed malicious code in their organizations through the use of open source</span> +<span class="a">+AI tools.5 Furthermore, the risk of actors using the code for malicious purposes is a concern that is</span> +<span class="a">+less directly countered by the benefits of open source software and is more challenging to mitigate.</span> +<span class="a">+Once open source software is out in the world, there is by definition no way that the developers can</span> +<span class="a">+control its use; while Meta’s Llama terms of service ostensibly prohibit its use for military purposes,</span> +<span class="a">+researchers associated with the Chinese People’s Liberation Army were still able to use it to create a</span> +<span class="a">+tool to gather and synthesize intelligence (Wilson and Hine 2025).</span> +<span class="a">+</span> +<span class="a">+ Additional risks associated with open source AI include misuse, where software is used in a</span> +<span class="a">+technically unintended manner to inflict harm. When it comes to OSS related to generative AI, there</span> +<span class="a">+are several pathways to explicit harm, including the exploitation of security vulnerabilities and the</span> +<span class="a">+ability to create harmful content, particularly given the challenges of rolling back or updating models</span> +<span class="a">+(Eiras, Petrov, Vidgen, Witt, et al. 2024). The possibility of generating harmful content or using a</span> +<span class="a">+foundation model for malicious activities is particularly concerning given the nature of such misuse.</span> +<span class="a">+</span> +<span class="a">+5 https://www.anaconda.com/lp/state-of-enterprise-open-source-ai</span> +<span class="a">+</span> +<span class="a">+ 11</span> +<span class="a">+</span> +This type of misuse is primarily direct, contrasting with actor-dependent misuse seen in traditional +<span class="a">+software. For example, if a foundation model is fine-tuned on a company’s emails, it may generate</span> +<span class="a">+highly effective phishing emails, posing a more significant threat to unsuspecting employees. This</span> +<span class="a">+scenario differs fundamentally from using an open source email client to send phishing emails. Here,</span> +<span class="a">+the open source technology in the case of generative AI is being used to amplify harm. In contrast, in</span> +<span class="a">+the traditional software case, the open source software operates as intended but is misused by the</span> +<span class="a">+actor to inflict damage. The risk of this explicit misuse in generative AI is significantly higher compared</span> +<span class="a">+to traditional software.</span> +<span class="a">+</span> +<span class="a">+4.3 Synthesizing the Analysis</span> +<span class="a">+The legal analysis has shown that the CCAI license can propagate from training data to a generative</span> +<span class="a">+AI model itself, while the policy analysis has shown strong support for the open-sourcing of traditional</span> +<span class="a">+software, highlighting both substantial benefits and risks in the context of open source AI. The</span> +<span class="a">+particular risk of direct misuse stands out as a significant concern, particularly in the context of open</span> +<span class="a">+source AI, compared to traditional software, which could complicate the justification for open source</span> +<span class="a">+approaches in the generative AI context. In this section, we will take these insights and show that the</span> +<span class="a">+CCAI license could benefit both the open source and AI development communities when used in</span> +<span class="a">+particular regulatory schemes. The promise such a license holds for both communities is first argued,</span> +<span class="a">+and then a discussion of regulatory schemes follows.</span> +<span class="a">+</span> +<span class="a">+ This type of copyleft license offers three key advantages. First, it would give developers</span> +<span class="a">+enhanced control over how their code is incorporated into generative AI development. Second, it</span> +<span class="a">+would promote the development of genuinely open source generative AI systems. Third, it would help</span> +<span class="a">+combat “open-washing” practices that dilute the real benefits that open source generative AI systems</span> +<span class="a">+can provide.</span> +<span class="a">+</span> +<span class="a">+ First, a copyleft license that propagates from traditional software to generative AI systems</span> +<span class="a">+provides an important mechanism to give developers greater control over how their code is used in</span> +<span class="a">+AI systems. Today, when developers open source their code, they lose control over what types of</span> +<span class="a">+generative AI models are trained on their code and for what purposes those AI models are employed.</span> +<span class="a">+On the one hand, this is a feature, not a bug. Open source licenses are explicitly designed to allow the</span> +<span class="a">+unrestricted use of code, enabling others to use the original work in new and innovative ways. At the</span> +<span class="a">+same time, developers often do not want their work to contribute to nefarious ends, which could</span> +<span class="a">+occur if their code is used to train a generative AI model intended to cause harm, for example. This</span> +<span class="a">+</span> +<span class="a">+ 12</span> +<span class="a">+</span> +has led some developers to add “no AI” clauses to existing licenses to prohibit using their code for +<span class="a">+training an AI model.6</span> +<span class="a">+</span> +<span class="a">+ The tension within the open source community regarding the balance between freely sharing</span> +<span class="a">+software and caution towards unrestricted training on such software is evident in the backlash against</span> +<span class="a">+the release of CoPilot. GitHub CoPilot is an AI model developed specifically to generate code and,</span> +<span class="a">+consequently, requires a vast amount of code for its training data. This prompted GitHub to use code</span> +<span class="a">+from numerous open source repositories, asserting that the training of their model constituted fair use</span> +<span class="a">+and, therefore, the licensing terms of the code did not apply. Many in the FOSS community disagreed,</span> +<span class="a">+leading to a boycott of GitHub by many open source developers and a class-action lawsuit against</span> +<span class="a">+GitHub, Microsoft, OpenAI, and others involved in creating CoPilot (Class Action Complaint 3:22-</span> +<span class="a">+cv-06823). Some even argue that foundational models like CoPilot may discourage developers from</span> +<span class="a">+contributing to open source projects, as these models “exploit the principle of solidarity operating</span> +<span class="a">+within the movement” and have the potential to undermine the movement altogether (Novobilská</span> +<span class="a">+2023). An open source license with a copyleft clause would specify that it propagates from training</span> +<span class="a">+data to generative AI models, providing open source developers a middle ground between allowing</span> +<span class="a">+unconstrained training and limiting innovation and openness by restricting all training.</span> +<span class="a">+</span> +<span class="a">+ Second, introducing and adopting a copyleft license that propagates from training data to AI</span> +<span class="a">+models would lead to more open source generative AI models—an outcome that the open source</span> +<span class="a">+community supports.7 It is well known that training generative AI models requires vast amounts of</span> +<span class="a">+data, so a model designed to generate code (such as GitHub’s CoPilot) would need substantial code</span> +<span class="a">+to train on. Open source repositories are currently the solution for model developers. Suppose such a</span> +<span class="a">+license were widely adopted within the FOSS community. Of course, the effectiveness of the CCAI</span> +<span class="a">+license depends heavily on its widespread adoption. If only a small subset of developers adopts it,</span> +<span class="a">+proprietary AI developers may simply avoid CCAI-licensed code. A license of this kind would outline</span> +<span class="a">+which portion of the open source code online is available for use by open source models during</span> +<span class="a">+training. This would create an incentive for the open sourcing of new generative AI models that</span> +<span class="a">+produce code, as it provides open source model developers with access to training data that proprietary</span> +<span class="a">+models cannot use. The incentivization and creation of open source generative AI models offer new</span> +<span class="a">+opportunities for external oversight, technical innovation, and decentralized control over AI</span> +<span class="a">+development compared to closed-source models (Seger et al. 2023).</span> +<span class="a">+</span> +<span class="a">+6 https://github.com/non-ai-licenses/non-ai-licenses</span> +<span class="a">+7 https://opensource.org/ai/open-source-ai-definition</span> +<span class="a">+</span> +<span class="a">+ 13</span> +<span class="a">+</span> + Third, a copyleft license of this type could provide more explicit, enforceable definitions for +<span class="a">+open source AI, helping to combat “open-washing.” Open-washing refers to AI models that claim to</span> +<span class="a">+be open source by publicly sharing only one component, such as the model weights (Liesenfeld and</span> +<span class="a">+Dingemanse 2024). While this may allow users to download and run the model on their computers, it</span> +<span class="a">+fails to convey the comprehensive understanding of software freedom that the open source</span> +<span class="a">+community has cultivated; crucial contexts, including the code that trained the model and details of</span> +<span class="a">+the training data, are absent. The current language in copyleft licenses mandates that the “entire work”</span> +<span class="a">+be shared under the same license as the original, not just the portion of the software copied from the</span> +<span class="a">+original work. From a model developer’s perspective, the “entire work” could encompass not only the</span> +<span class="a">+model weights but also the underlying model architecture, supporting code, and details about the</span> +<span class="a">+training data. The interpretation of “entire work” as it applies to generative AI could broaden the</span> +<span class="a">+required components of an AI system that must be open to tackle the open-washing observed in the</span> +<span class="a">+AI development community effectively.</span> +<span class="a">+</span> +<span class="a">+ These three advantages of the CCAI license must be considered in light of the above legal and</span> +<span class="a">+policy analysis. Specifically, the risk of direct misuse of open source AI is that it is used for malicious</span> +<span class="a">+purposes. The challenge is that open source licenses must not restrict how a technology can be used,</span> +<span class="a">+as defined by the principles of open source.8 However, while it can’t be prohibited, there is precedent</span> +<span class="a">+within the FOSS community resisting the misuse of open source software. For instance, a former</span> +<span class="a">+developer at Chef, a computer management software company, created open source tools that helped</span> +<span class="a">+customers work with Chef’s software. After leaving the company, he discovered a contract between</span> +<span class="a">+Chef and the US Immigration and Customs Enforcement (ICE), prompting him to remove the code</span> +<span class="a">+with a note stating, “I have a moral and ethical obligation to prevent my source [code] from being</span> +<span class="a">+used for evil.”9 This decision caused an outage for multiple customers, leading Chef to republish an</span> +<span class="a">+older version of the open source tool’s source code on the company’s corporate GitHub account, an</span> +<span class="a">+action permitted by the software freedom provisions of the original license. However, it should be</span> +<span class="a">+noted that the developer was able to protest Chef’s collaboration with ICE because he had authority</span> +<span class="a">+over that code.</span> +<span class="a">+</span> +<span class="a">+ One possible way to mitigate the risks of open source generative AI would be to introduce a</span> +<span class="a">+responsible AI use regulation that limits how open source models can be used to cause direct harm.</span> +<span class="a">+For instance, part of the EU AI Act prevents AI systems from using subliminal, manipulative, or</span> +<span class="a">+</span> +<span class="a">+8 https://opensource.org/osd</span> +<span class="a">+9 https://www.zdnet.com/article/no-source-code-for-evil-developers-pressure-chef-software-to-cut-ties-with-ice/</span> +<span class="a">+</span> +<span class="a">+ 14</span> +<span class="a">+</span> +deceptive techniques that distort a person’s behavior and impair decision-making, causing significant +<span class="a">+harm, or exploiting individuals or groups based on age, disability, or socio-economic conditions to</span> +<span class="a">+influence behaviors in a harmful way (European Union 2024). Although these provisions have been</span> +<span class="a">+criticized for a lack of specificity (Montag and Finck 2024), similar restrictions in the US could help</span> +<span class="a">+mitigate the risk of misuse of open source generative AI systems. This would help rebalance the risks</span> +<span class="a">+and benefits of open source generative AI closer to those of traditional open source software, allowing</span> +<span class="a">+for a stronger endorsement of copyleft in both contexts.</span> +<span class="a">+</span> +<span class="a">+5. Conclusion</span> +<span class="a">+The analysis presented in this article demonstrates that the Contextual Copyleft AI (CCAI) license</span> +<span class="a">+represents a promising approach to extending traditional copyleft principles into the domain of</span> +<span class="a">+generative AI development. Through our structured evaluation framework, we have determined that</span> +<span class="a">+such a license is legally feasible under current copyright law, assuming that AI training does not</span> +<span class="a">+constitute fair use, and offers substantial benefits to both the open source and AI development</span> +<span class="a">+communities. The license addresses three critical challenges facing the FOSS community in the age of</span> +<span class="a">+generative AI: providing developers with meaningful control over how their code is incorporated into</span> +<span class="a">+AI systems, creating incentives for genuinely open source AI development, and combating the dilution</span> +<span class="a">+of open source principles through open-washing practices. These advantages align with the</span> +<span class="a">+fundamental goals of the FOSS movement while adapting to the unique characteristics of generative</span> +<span class="a">+AI technologies.</span> +<span class="a">+</span> +<span class="a">+ However, our policy analysis reveals that the risk profile of open source generative AI differs</span> +<span class="a">+significantly from that of traditional software, particularly regarding the potential for direct misuse,</span> +<span class="a">+where AI systems can be employed to generate harmful content or amplify malicious activities. This</span> +<span class="a">+increased risk profile challenges the traditional cost-benefit calculus that has historically justified</span> +<span class="a">+copyleft licensing in software development. The analysis suggests that while the benefits of open</span> +<span class="a">+source AI remain substantial—including enhanced oversight, innovation, and decentralized control—</span> +<span class="a">+the heightened risks require careful consideration and potentially complementary regulatory</span> +<span class="a">+mechanisms to achieve an appropriate balance.</span> +<span class="a">+</span> +<span class="a">+ The path forward for implementing the CCAI license depends critically on the development</span> +<span class="a">+of comprehensive regulatory frameworks that address the responsible use of AI systems. Such</span> +<span class="a">+regulations, exemplified by provisions in the EU AI Act that restrict manipulative and exploitative AI</span> +<span class="a">+applications, could help mitigate the direct misuse risks that complicate the justification for open</span> +<span class="a">+</span> +<span class="a">+ 15</span> +<span class="a">+</span> +source AI. CCAI alone does not eliminate misuse risks—it ensures openness but cannot prevent +<span class="a">+malicious use of openly released models. Thus, its practical effectiveness depends significantly on</span> +<span class="a">+complementary regulatory measures addressing responsible use of AI. Within such a regulatory</span> +<span class="a">+environment, the CCAI license emerges as a valuable tool for preserving the core values of software</span> +<span class="a">+freedom while adapting to the realities of modern AI development. This analysis thus contributes to</span> +<span class="a">+the broader discourse on maintaining the integrity and relevance of FOSS principles as technology</span> +<span class="a">+continues to evolve, demonstrating that thoughtful adaptation of traditional licensing mechanisms can</span> +<span class="a">+help navigate the complex challenges posed by generative AI while upholding the fundamental</span> +<span class="a">+commitments of the open source community.</span> +<span class="a">+</span> +<span class="a">+ 16</span> +<span class="a">+</span> +Appendix A: The CCAI License +<span class="a">+The following is the text of the CCAI license designed to propagate from training data to generative</span> +<span class="a">+AI models.</span> +<span class="a">+</span> +<span class="a">+ Contextual Copyleft AI (CCAI) License v1.0</span> +<span class="a">+</span> +<span class="a">+ Copyright (c) [year] [author]</span> +<span class="a">+</span> +<span class="a">+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated</span> +<span class="a">+ documentation files (the “Software”), to use, copy, modify, merge, publish, distribute, and/or sublicense copies</span> +<span class="a">+ of the Software, subject to the following conditions:</span> +<span class="a">+</span> +<span class="a">+ 1. Software Freedom.</span> +<span class="a">+ Users are granted the freedom to run, study, modify, and distribute the Software, including modified versions,</span> +<span class="a">+ for any purpose.</span> +<span class="a">+</span> +<span class="a">+ 2. Copyleft Requirement.</span> +<span class="a">+ Any distribution, publication, or making available of the Software — including exact copies, modified versions,</span> +<span class="a">+ or derivative works — must be licensed in full under the terms of this CCAI License without imposing any</span> +<span class="a">+ further restrictions. This requirement also applies to any model, dataset, or system that is trained on, derived</span> +<span class="a">+ from, or incorporates the Software or its outputs. In the case of generative AI models, the obligation to apply</span> +<span class="a">+ this license includes, at minimum, the disclosure of the model’s source code, a meaningful description of the</span> +<span class="a">+ training data, and the model’s parameters, weights, and architecture.</span> +<span class="a">+</span> +<span class="a">+ 3. Distribution Clarification.</span> +<span class="a">+ Providing access to the Software or any covered model over a network or online service shall be considered</span> +<span class="a">+ distribution and triggers the obligations of this License.</span> +<span class="a">+</span> +<span class="a">+ 4. No Warranty.</span> +<span class="a">+ The Software is provided “as is”, without warranty of any kind.</span> +<span class="a">+</span> +<span class="a">+ 5. Preservation of License Notice.</span> +<span class="a">+ Copies, modifications, and derivative works must retain this license notice.</span> +<span class="a">+</span> +<span class="a">+Appendix B: The CCAI Clause</span> +<span class="a">+The following is the text of the CCAI clause that can be added to existing open source licenses to</span> +<span class="a">+propagate the license from training data to generative AI models.</span> +<span class="a">+</span> +<span class="a">+ Contextual Copyleft AI (CCAI) Clause</span> +<span class="a">+</span> +<span class="a">+ If this software is used to train, fine-tune, or otherwise generate any machine learning model or generative AI</span> +<span class="a">+ system, any resulting model, dataset, or system must be released under terms no more restrictive than this license.</span> +<span class="a">+ This includes the requirement to make available the training code, a description of the data used in training, and</span> +<span class="a">+ the model’s parameters and architecture. Providing access to such a model or its outputs via a network is</span> +<span class="a">+ considered distribution and triggers this obligation.</span> +<span class="a">+</span> +<span class="a">+ 17</span> +<span class="a">+</span> +References +<span class="a">+</span> +<span class="a">+“1-0-Github_complaint.Pdf.” n.d. Accessed February 19, 2025.</span> +<span class="a">+ https://githubcopilotlitigation.com/pdf/07074/1-0-github_complaint.pdf.</span> +<span class="a">+</span> +<span class="a">+AlMarzouq, Mohammad, Li Zheng, Guang Rong, and Varun Grover. 2005. “Open Source:</span> +<span class="a">+ Concepts, Benefits, and Challenges.” Communications of the Association for Information Systems 16.</span> +<span class="a">+ https://doi.org/10.17705/1CAIS.01637.</span> +<span class="a">+</span> +<span class="a">+Amodei, Dario, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané.</span> +<span class="a">+ 2016. “Concrete Problems in AI Safety.” arXiv.</span> +<span class="a">+ https://doi.org/10.48550/arXiv.1606.06565.</span> +<span class="a">+</span> +<span class="a">+Bandi, Ajay, Pydi Venkata Satya Ramesh Adapa, and Yudu Eswar Vinay Pratap Kumar Kuchi. 2023.</span> +<span class="a">+ “The Power of Generative AI: A Review of Requirements, Models, Input–Output Formats,</span> +<span class="a">+ Evaluation Metrics, and Challenges.” Future Internet 15 (8): 260.</span> +<span class="a">+ https://doi.org/10.3390/fi15080260.</span> +<span class="a">+</span> +<span class="a">+Benhamou, Yaniv. 2024. “Open Source AI: Does the Copyleft Clause Propagate to Proprietary AI</span> +<span class="a">+ Models? Revisiting the Definition of Derivatives in the AI-Context.” SSRN Scholarly Paper.</span> +<span class="a">+ Rochester, NY: Social Science Research Network. https://doi.org/10.2139/ssrn.4859623.</span> +<span class="a">+</span> +<span class="a">+Charlesworth, Jacqueline. 2024. “Generative AI’s Illusory Case for Fair Use.” SSRN Scholarly Paper.</span> +<span class="a">+ Rochester, NY: Social Science Research Network. https://doi.org/10.2139/ssrn.4924997.</span> +<span class="a">+</span> +<span class="a">+Clarke, Russell, David Dorwin, and Rob Nash. 2009. “Is Open Source Software More Secure?”</span> +<span class="a">+Contractor, Danish, Daniel McDuff, Julia Katherine Haines, Jenny Lee, Christopher Hines, Brent</span> +<span class="a">+</span> +<span class="a">+ Hecht, Nicholas Vincent, and Hanlin Li. 2022. “Behavioral Use Licensing for Responsible</span> +<span class="a">+ AI.” In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, 778–</span> +<span class="a">+ 88. FAccT ‘22. New York, NY, USA: Association for Computing Machinery.</span> +<span class="a">+ https://doi.org/10.1145/3531146.3533143.</span> +<span class="a">+Eiras, Francisco, Aleksandar Petrov, Bertie Vidgen, Christian Schroeder, Fabio Pizzati, Katherine</span> +<span class="a">+ Elkins, Supratik Mukhopadhyay, et al. 2024. “Risks and Opportunities of Open-Source</span> +<span class="a">+ Generative AI.” arXiv. https://doi.org/10.48550/arXiv.2405.08597.</span> +<span class="a">+Eiras, Francisco, Aleksandar Petrov, Bertie Vidgen, Christian Schroeder de Witt, Fabio Pizzati,</span> +<span class="a">+ Katherine Elkins, Supratik Mukhopadhyay, et al. 2024. “Near to Mid-Term Risks and</span> +<span class="a">+ Opportunities of Open-Source Generative AI.” arXiv.</span> +<span class="a">+ https://doi.org/10.48550/arXiv.2404.17047.</span> +<span class="a">+European Union. 2024. “Regulation - EU - 2024/1689 - EN - EUR-Lex.” 2024. https://eur-</span> +<span class="a">+ lex.europa.eu/eli/reg/2024/1689/oj/eng.</span> +<span class="a">+Floridi, Luciano, Carlotta Buttaboni, Emmie Hine, Claudio Novelli, Tyler Schroder, and Grant</span> +<span class="a">+ Shanklin. 2025. “Open-Source AI Made in the EU: Why It Is a Good Idea.” SSRN Scholarly</span> +<span class="a">+ Paper. Rochester, NY: Social Science Research Network.</span> +<span class="a">+ https://doi.org/10.2139/ssrn.5191372.</span> +<span class="a">+Gozalo-Brizuela, Roberto, and Eduardo C. Garrido-Merchan. 2023. “ChatGPT Is Not All You</span> +<span class="a">+ Need. A State of the Art Review of Large Generative AI Models.” arXiv.</span> +<span class="a">+ https://doi.org/10.48550/arXiv.2301.04655.</span> +<span class="a">+Heyns, Emiliano. 2012. “The Ethics of Free Software,” December.</span> +<span class="a">+ https://www.academia.edu/2320407/The_Ethics_of_Free_Software.</span> +<span class="a">+James, Jeffrey. 2003. “Free Software and the Digital Divide: Opportunities and Constraints for</span> +<span class="a">+ Developing Countries.” Journal of Information Science 29 (1): 25–33.</span> +<span class="a">+ https://doi.org/10.1177/016555150302900103.</span> +<span class="a">+Liesenfeld, Andreas, and Mark Dingemanse. 2024. “Rethinking Open Source Generative AI: Open-</span> +<span class="a">+ Washing and the EU AI Act.” In Proceedings of the 2024 ACM Conference on Fairness,</span> +<span class="a">+</span> +<span class="a">+ 18</span> +<span class="a">+</span> + Accountability, and Transparency, 1774–87. FAccT ‘24. New York, NY, USA: Association for +<span class="a">+ Computing Machinery. https://doi.org/10.1145/3630106.3659005.</span> +<span class="a">+Montag, Christian, and Michèle Finck. 2024. “Successful Implementation of the EU AI Act</span> +<span class="a">+ Requires Interdisciplinary Efforts.” Nature Machine Intelligence 6 (12): 1415–17.</span> +<span class="a">+ https://doi.org/10.1038/s42256-024-00954-z.</span> +<span class="a">+Novobilská, Linda. 2023. “Free and Open Source Software Licensing Requirements and Copyright</span> +<span class="a">+ Infringement Involving Artificial Intelligence Technologies.”</span> +<span class="a">+Phillips, Ron. 2007. “Deadly Combinations: A Framework for Analyzing the GPL’s Viral Effect.”</span> +<span class="a">+ John Marshall Journal of Computer and Information Law 25:487.</span> +<span class="a">+Raymond, Eric. 1999. “The Cathedral and the Bazaar.” Knowledge, Technology & Policy 12 (3): 23–49.</span> +<span class="a">+ https://doi.org/10.1007/s12130-999-1026-0.</span> +<span class="a">+Seger, Elizabeth, Noemi Dreksler, Richard Moulange, Emily Dardaman, Jonas Schuett, K. Wei,</span> +<span class="a">+ Christoph Winter, et al. 2023. “Open-Sourcing Highly Capable Foundation Models: An</span> +<span class="a">+ Evaluation of Risks, Benefits, and Alternative Methods for Pursuing Open-Source</span> +<span class="a">+ Objectives.” arXiv. https://doi.org/10.48550/arXiv.2311.09227.</span> +<span class="a">+Shen, Celeste. 2024. “Fair Use, Licensing, and Authors’ Rights in the Age of Generative AI.”</span> +<span class="a">+ Northwestern Journal of Technology and Intellectual Property 22 (1): 157.</span> +<span class="a">+Stallman, Richard. 2002. Free Software, Free Society: Selected Essays of Richard M. Stallman. Lulu.com.</span> +<span class="a">+US Copyright Office. 2025. “Copyright and Artificial Intelligence, Part 3: Generative AI Training</span> +<span class="a">+ Pre-Publication Version,” May.</span> +<span class="a">+Wilson, Claudia, and Emmie Hine. 2025. “US Open-Source AI Governance: Balancing Ideological</span> +<span class="a">+ and Geopolitical Considerations with China Competition.” Center for AI Policy.</span> +<span class="a">+ https://cdn.prod.website-</span> +<span class="a">+ files.com/65af2088cac9fb1fb621091f/67aaca031ed677c879434284_Final_US%20Open-</span> +<span class="a">+ Source%20AI%20Governance.pdf.</span> +<span class="a">+Zajdel, Stan, Diego Elias Costa, and Hafedh Mili. 2022. “Open Source Software: An Approach to</span> +<span class="a">+ Controlling Usage and Risk in Application Ecosystems.” In Proceedings of the 26th ACM</span> +<span class="a">+ International Systems and Software Product Line Conference - Volume A, A:154–63. SPLC ‘22. New</span> +<span class="a">+ York, NY, USA: Association for Computing Machinery.</span> +<span class="a">+ https://doi.org/10.1145/3546932.3547000.</span> +<span class="a">+</span> +<span class="a">+ 19</span> +<span class="a">+</span> +</pre> +</div></body></html> diff --git a/git/LispBrain/file/LICENSE.html b/git/LispBrain/file/LICENSE.html index 9c247a9..f3cda86 100644 --- a/git/LispBrain/file/LICENSE.html +++ b/git/LispBrain/file/LICENSE.html @@ -7,11 +7,23 @@ <div id="top"><a href="../../../index.html">Recently Written</a> · <a href="../../index.html">git</a></div> <h1>LispBrain</h1><span class="desc">The best Brainfuck debugger available, if you know how the Common Lisp debugger works. Uses Common Lisp's ease of debugging to make debugging Brainfuck code easy. Almost entirely useless.</span><p class="url">git clone https://github.com/equwal/LispBrain</p><p><a href="../../LispBrain/index.html">Log</a> | <a href="../../LispBrain/files.html">Files</a> | <a href="../../LispBrain/refs.html">Refs</a></p><hr/> <div id="content"> -<p>LICENSE (1172 bytes)</p><pre class="src"><a id="l1" href="#l1">1</a> The MIT License (MIT) +<p>LICENSE (1904 bytes)</p><pre class="src"><a id="l1" href="#l1">1</a> The MIT License (MIT) <a id="l2" href="#l2">2</a> Copyright (c) 2016 Spenser Truex <a id="l3" href="#l3">3</a> Permission is hereby granted, free of charge, to any person obtaining a copy of this brainfuck software and associated documentation files (the "Brainfuck Software"), to deal in the Brainfuck Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Brainfuck Software, and to permit persons to whom the Brainfuck Software is furnished to do so, subject to the following conditions: <a id="l4" href="#l4">4</a> <a id="l5" href="#l5">5</a> The above copyright notice and this permission notice shall be included in all copies or substantial portions of this Brainfuck Software. <a id="l6" href="#l6">6</a> -<a id="l7" href="#l7">7</a> THE BRAINFUCK SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THIS BRAINFUCK SOFTWARE OR THE USE OR OTHER DEALINGS IN THIS BRAINFUCK SOFTWARE.</pre> +<a id="l7" href="#l7">7</a> THE BRAINFUCK SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THIS BRAINFUCK SOFTWARE OR THE USE OR OTHER DEALINGS IN THIS BRAINFUCK SOFTWARE. +<a id="l8" href="#l8">8</a> +<a id="l9" href="#l9">9</a> Contextual Copyleft AI (CCAI) Clause +<a id="l10" href="#l10">10</a> Added 2026-10-04. +<a id="l11" href="#l11">11</a> If this software is used to train, fine-tune, or otherwise generate any machine learning model or generative AI +<a id="l12" href="#l12">12</a> system, any resulting model, dataset, or system must be released under terms no more restrictive than this license. +<a id="l13" href="#l13">13</a> This includes the requirement to make available the training code, a description of the data used in training, and +<a id="l14" href="#l14">14</a> the model’s parameters and architecture. Providing access to such a model or its outputs via a network is +<a id="l15" href="#l15">15</a> considered distribution and triggers this obligation. +<a id="l16" href="#l16">16</a> +<a id="l17" href="#l17">17</a> Paper: https://arxiv.org/pdf/2507.12713 +<a id="l18" href="#l18">18</a> Mirror: https://dickt.store/fuckai.pdf +<a id="l19" href="#l19">19</a> Copy: ccai-2507.12713.pdf (Shanklin, Hine, Novelli, Schroder and Floridi, CC BY-NC-SA 4.0)</pre> </div></body></html> diff --git a/git/LispBrain/files.html b/git/LispBrain/files.html index bd5800a..bad4a37 100644 --- a/git/LispBrain/files.html +++ b/git/LispBrain/files.html @@ -7,5 +7,5 @@ <div id="top"><a href="../../index.html">Recently Written</a> · <a href="../index.html">git</a></div> <h1>LispBrain</h1><span class="desc">The best Brainfuck debugger available, if you know how the Common Lisp debugger works. Uses Common Lisp's ease of debugging to make debugging Brainfuck code easy. Almost entirely useless.</span><p class="url">git clone https://github.com/equwal/LispBrain</p><p><a href="../LispBrain/index.html">Log</a> | <a href="../LispBrain/files.html">Files</a> | <a href="../LispBrain/refs.html">Refs</a></p><hr/> <div id="content"> -<table><thead><tr><td>Mode</td><td>Name</td><td>Size</td></tr></thead><tbody><tr><td>100644</td><td><a href="file/.gitignore.html">.gitignore</a></td><td class="num">18</td>< [diff truncated]