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- CC signals are aimed at demanding reciprocal action by AI developers<\/strong>. What does reciprocity in the AI ecosystem look like to you? How can we improve the proposed signals to better achieve reciprocity?<\/li>\n
- Community governance will be key to determining when and how CC signals are applied<\/strong>. How do you think decisions by content stewards should be made? Whose preferences matter in these decisions?<\/li>\n
- CC signals prioritize standardization and machine readability to increase their utility, but this comes with costs<\/strong>. How do you think we should consider the tradeoff between context-specific considerations and the goal of collective action that spurs behavioral change by reusers operating at a massive scale?<\/li>\n<\/ul>\n
Get involved by <\/span>sharing your feedback<\/span><\/a>.\u00a0<\/span><\/p>\n
The CC Signals<\/h2>\n
\ud83c\udfc1Start here: If you haven\u2019t already, read through the context and considerations<\/a> that are informing the development of CC signals. You can also download our report<\/a>.<\/p>\n
Now that you have the background, let\u2019s dig into the details.<\/p>\n
The idea behind CC signals is simple. Using CC signals, a steward of a large collection of content can express a set of criteria that AI developers must meet. The criteria are organized around different dimensions of reciprocity, and are intended to drive meaningful, practical action.<\/p>\n
CC signals are designed to be interpretable by machines, as well as humans.\u00a0<\/span><\/p>\n
The Suite of CC Signals<\/h3>\n
![\"\"](\"https:\/\/creativecommons.org\/wp-content\/uploads\/2025\/06\/The-Suite-of-CC-Signals_1-e1750710313534.png\")
![\"\"](\"https:\/\/creativecommons.org\/wp-content\/uploads\/2025\/06\/The-Suite-of-CC-Signals_2-e1750710325526.png\")
![\"\"](\"https:\/\/creativecommons.org\/wp-content\/uploads\/2025\/06\/The-Suite-of-CC-Signals_3-e1750710336871.png\")
![\"\"](\"https:\/\/creativecommons.org\/wp-content\/uploads\/2025\/06\/Screenshot-2025-06-23-170208-e1750712571450.png\")
<\/p>\nThis project draws inspiration from fundamental concepts often referenced in the AI debate\u2014consent, compensation, and credit\u2014 but with a particular angle. Our approach is driven by the goal of increasing and sustaining public access to knowledge.\u00a0<\/span><\/p>\n
Each signal includes the conditions by which content can be used for machine reuse. These are criteria that AI developers must meet in order to use the content for AI development.\u00a0 All of the criteria are designed to promote reciprocity in ways that are both meaningful and practical given the scale of machine reuse. Our initial proposal includes the following signal elements:<\/p>\n
\u00a0Credit<\/strong>: You must give appropriate credit based on the method, means, and context of your use.<\/p>\n\u00a0Direct Contribution: <\/strong>You must provide monetary or in-kind support to the Declaring Party for their development and maintenance of the assets, based on a good faith valuation taking into account your use of the assets and your financial means.<\/p>\nEcosystem Contribution: <\/strong>You must provide monetary or in-kind support back to the ecosystem from which you are benefiting, based on a good faith valuation taking into account your use of the assets and your financial means.<\/p>\nOpen: <\/strong>The AI system used must be open. For example, AI systems must satisfy the Model Openness Framework (MOF) Class II, MOF Class I, or the Open Source AI Definition (OSAID).<\/p>\n\ud83d\uddd2\ufe0fNote: Credit is included in each signal because we believe it is a fundamental form of reciprocity, one that benefits the broader knowledge cycle. In this proposal, the other signals are mutually exclusive. The list of signals is intentionally limited so that the collective of data stewards and their communities data holding communities can align in calling for their adoption with AI developers. This will ultimately build networks for collective action, requiring reciprocity within the AI ecosystem.<\/p>\n
How the CC Signals Work<\/h2>\n
Who is applying the signal:<\/strong><\/p>\n
A Declaring Party<\/strong> is someone who specifies how a content collection should be used by machines. Sometimes, the Declaring Party will hold copyright or have authority to represent rightsholders in the content. In these cases, a CC signal may have legal effect depending on the particular jurisdiction. In cases where a collection of content includes content from multiple authors, it will be the responsibility of the Declaring Party to coordinate among its community to determine the appropriate signal(s).<\/p>\n
The scope of machine uses addressed by the signal:<\/strong><\/p>\n
The Declaring Party applies CC signals to a set of standard categories that encompass machine use, from general categories to more specific categories, such as Text and Data Mining, AI Training, Generative AI Training, and AI Inference. In order to maximize global interoperability, these categories will not be defined by Creative Commons. Instead, they will be based upon global standards being developed by the Internet Engineering Task Force (IETF). The CC signals framework is designed to evolve as the standard categories are finalized. The selected category makes up the scope of what activity the tool is intended to address.<\/p>\n
What signal is applied:<\/strong><\/p>\n
The Declaring Party selects among the available CC signals. Once selected, the signal reflects the Declaring Party’s preferences regarding machine reuse. This means that the Declaring Party says that the selected category of machine reuse is allowed under the terms of the particular signal elements. The four proposed signal combinations are:<\/p>\n
Credit
\nCredit + Direct Contribution
\nCredit + Ecosystem Contribution
\nCredit + Open<\/p>\nSimilar to the CC licenses, CC signals will be both machine and human readable. The human-readable explanation of what happens when a signal is applied will be called a declaration<\/b>. There will be a declaration for each signal, with variations based on whether the Declaring Party has copyright authority and the particular scope of machine reuse selected. <\/span>The string of code used to apply a CC signal to a dataset will be called a content usage expression<\/b>.<\/p>\n
Legal Considerations<\/h2>\n
CC signals are designed as global tools, which means they operate across legal systems that work differently. In the context of machine reuse, copyright law is limited, uncertain, and inconsistent across jurisdictions. <\/span>As a result, applying a CC signal is likely to have a different legal effect depending on who applies it and in what context.<\/p>\n
Where copyright exists and is applicable, CC signals are intended to leverage the power of copyright without increasing its power.\u00a0<\/span><\/p>\n
This is not about creating new property rights; it is more like defining <\/span>manners for machines.<\/span><\/i><\/p>\n
For more detail, please see the <\/span>report<\/span><\/a>. Further research and analysis about the legal implications of CC signals will be a major focus of our efforts in the coming months.<\/span><\/p>\n
Technical Considerations<\/h2>\n
- Community governance will be key to determining when and how CC signals are applied<\/strong>. How do you think decisions by content stewards should be made? Whose preferences matter in these decisions?<\/li>\n
![\"\"](\"https:\/\/creativecommons.org\/wp-content\/uploads\/2025\/06\/Adhering-to-CC-Signals-1-e1750711744912.png\")
Credit Signal<\/h3>\n![\"\"](\"https:\/\/creativecommons.org\/wp-content\/uploads\/2025\/06\/Adhering-to-CC-Signals_3-e1750711161429.png\")
Direct Contribution<\/h3>\n![\"\"](\"https:\/\/creativecommons.org\/wp-content\/uploads\/2025\/06\/Adhering-to-CC-Signals_2-e1750711100729.png\")
Ecosystem Contribution Signals<\/h3>\n![\"\"](\"https:\/\/creativecommons.org\/wp-content\/uploads\/2025\/06\/Adhering-to-CC-Signals_4-e1750711220571.png\")
Open Signal<\/h3>\n