Creative Commons Introduces CC Signals: A New Social Contract for the Age of AI

TL;DR – What are CC signals?

CC signals are a proposed framework to help content stewards express how they want their works used in AI training—emphasizing reciprocity, recognition, and sustainability in machine reuse. They aim to preserve open knowledge by encouraging responsible AI behavior without limiting innovation.

💗Love it! How can I show my support? Thank you! There are multiple ways that you can show your support including:

• Fund: Make an annual recurring donation via our Open Infrastructure Circle. This work will require a large amount of resourcing, over many years, to make happen.
• Join: Express early interest in supporting or implementing CC signals by getting involved in the next steps of development. 
• Amplify: Invite a CC expert to present, join a panel, or give a keynote address on AI and the commons, sharing in the age of AI, AI and copyright, and CC signals by sending an email to [email protected].

🔍I’m interested. I want to learn more. We’ve got you! For a detailed analysis, download our report From Human Content to Machine Data: Introducing CC Signals. To dig into the more technical components of CC signals, head over to the CC Signals Implementation page.

Why CC Signals: Context & Considerations

This is an extremely complex challenge and the stakes are huge. Many intersecting and interconnected solutions are required, and thankfully, there are many public interest organizations joined together in this fight. CC’s involvement in only one part of the puzzle, but a critical one.

Here’s what we’ve been analyzing to inform our proposed solution.

AI depends on public web data. But who sets the rules?

The data that powers AI was created by people and communities. Today, billions of webpages fuel AI systems. This has happened quickly at an unprecedented scale. It has also happened without the involvement of content creators and stewards, reaching beyond people’s reasonable expectations for how their works would be used when they shared them publicly.

Machine use of web content is not new. So what’s changed?

Machines have long accessed and compiled web content to build search engines and digital archives. However, today, machines don’t just crawl the web to make it more searchable or to help unlock new insights—they feed algorithms that fundamentally change (and threaten) the web we know.

AI is outpacing the social contract. Why does this matter for the commons?

The norms that govern how machines use data are out of date and under threat. The current AI ecosystem is out of alignment with the social contract that has long governed the digital commons: we share openly, but we do so expecting respect, recognition, and reciprocity. This has resulted in (understandable) backlash against advances in AI that range from various types of enclosure to simply not sharing at all.

If everyone blocks access, everyone loses. How can we avoid this path?

The future of the commons is now under threat. This isn’t sustainable, and it isn’t leading to the future we want. The commons is one of our greatest shared assets. Barrier-free access to knowledge underpins scientific discovery, democracy, and acts as an antidote to mis and disinformation. If content is no longer publicly available or otherwise becomes more risky and uncertain to use, it becomes solely accessible to those with deep pockets. In addition to impeding human access to knowledge, we’re concerned that a shift to restrictive licensing would result in a less fair, diverse, and competitive AI ecosystem.

Copyright law was never meant to do this. So what’s the solution?

Ideas, facts, and other building blocks of knowledge cannot be owned. Expanding copyright to control AI training risks stifling innovation and access to knowledge. The future depends on shared expectations and responsible reuse. Any viable solution needs to be legally grounded, technically interoperable, and backed by the collective action of humans. We need a new social contract for the age of AI. This isn’t just about datasets or licenses — it’s about safeguarding open knowledge, trust, and equity in the digital age.

📘 Dive Deeper
Want the full context behind CC signals?
👉 Read From Human Content to Machine Data: Introducing CC Signals

We’re Fighting for the Commons: CC Signals Is Part of the Solution

The development of CC signals is based on:

• The belief that there are many legitimate purposes for machine reuse of content that must be protected;
• An ecosystem that better addresses the legitimate concerns of those creating and stewarding human knowledge is both possible and necessary.

CC signals draw inspiration from fundamental concepts often referenced in the AI debate—consent, compensation, and credit—but with a particular angle. Our approach is driven by the goal of increasing and sustaining public access to knowledge.

The proposed CC signal elements are structured to reflect different dimensions of reciprocity: credit, financial sustainability, and non-monetary forms of contribution. They do not aim to limit or restrict the types of AI training or other types of uses (for example,  text and data mining) that machines can undertake. Instead, they are designed to incentivize actions in return.

🕵️Curious how CC signals could reshape the future of AI?

Dive into our early thinking—then help shape what comes next! We’re looking for your ideas, feedback, and questions on the legal, technical, and social layers of this work.

Collective Action

Social norms are arguably the single most important aspect of human governance. They dictate how we behave, how we belong, and how we make decisions across nearly every aspect of our lives.

Norms can be powerful, but they require collective action. We’re wary of creators and collections of content each trying to shape how their works are used in thousands of different, incompatible ways. A single preference, uniquely expressed, is inconsequential in the machine age.

That doesn’t mean individual voices don’t matter. In many cases, a single collection will contain works by many contributors.

Power comes from coordination and solidarity. The more we align across sectors, communities, and geographies, the more leverage we gain to influence AI policy and practice.