In this post, we outline our plans to build upon and strengthen CC signals in order to support our goal of sustained access to human knowledge. We do not have all the answers yet. What we do have is a framework for how we will work toward them.

Recap: What’s At Stake

When it comes to AI, copyright operates in a landscape that is uneven and often unclear. Because of this, the CC licenses, while still important, are not sufficient to address how content is used in AI systems. You can read more on this here. CC licenses also do not fully capture the range of intentions creators and data holders have in an AI-mediated world.

Across the web, creators, communities, and institutions are turning to multiple forms of defensive enclosure to restrict access. These include:

• Legal (e.g. licensing), such as open access publishers recommending CC BY-NC-ND as a mechanism of control, which ACM now does, which negatively impacts human collaboration.
• Technical (e.g. CAPTCHAs, bot blocking, rate limiting), such as what news publishers are doing, which negatively impacts archiving efforts.
• Financial (e.g. paywalled APIs), such as what X did post-acquisition, which negatively impacts researchers. 

The problem is that these tools treat all machine use as the same, regardless of the purpose. In trying to limit large-scale extraction by AI developers, they also block public interest uses like research, preservation, and accessibility.

While our research is ongoing, there are early indications of a more fragmented and potentially shrinking commons, along with a weakening of long-standing public interest protections.

Building the Next Generation Infrastructure of Sharing

Open access through CC licenses created a spectrum of sharing. Today we need something similar for AI: a spectrum of participation, where creators and data-holding stewards are active participants in how knowledge is produced, shared, and used.

The commons we have built over the past 25 years did not emerge on its own. It was designed through legal frameworks, technical standards, and shared norms. The AI era requires the next generation of that infrastructure. We want a future where the global knowledge commons remains accessible, and where AI systems engage with it in ways that are transparent, accountable, and aligned with the public good.

Our Plans

CC is advancing several high-impact interventions as part of the CC signals framework to restore trust, strengthen participation, and embed public interest values into the AI knowledge ecosystem.

1. Helping People Make Informed Decisions in the Current Moment
2. Making Attribution the Norm in AI
3. Building New Tooling that Protects Public Interest Uses while Restoring Agency

Helping People Make Informed Decisions in the Current Moment

AI systems are using CC-licensed works in ways that are causing many to question whether the existing CC license suite still aligns with their goals.

These concerns take different forms: attribution that disappears inside AI systems, sensitive knowledge stripped from its original context, growing concentrations of value and power, and no clear mechanisms for reciprocity or accountability. But they share a common root: uncertainty about what the CC licenses actually mean in this new environment.

We want people who choose to CC license to do so with confidence. We also want institutions with CC licensing embedded in their policies to have a clear picture of what the licenses do and do not cover when it comes to AI.  Over the next six months, we will provide sector-specific interim guidance to support CC licensors in navigating the new questions that AI raises for them. This guidance is not intended to resolve all legal ambiguity. Instead, during this period of uncertainty, we want to preserve the practice of sharing that AI is currently putting at risk, while we develop new tools and practices that address our communities’ concerns.

We will be holding a series of sector-specific virtual events to collect feedback on this interim guidance. Sign up for the CC newsletter for more information as soon as it becomes available. 

Making Attribution the Norm in AI

Attribution has always been a cornerstone of the commons. It supports participation, enables transparency, and allows knowledge to be traced, evaluated, and built upon.

Today’s AI ecosystem is eroding this norm. Most generative systems do not meaningfully acknowledge the sources they rely on. As AI increasingly mediates access to knowledge, this has serious consequences: loss of provenance, reduced trust, and fewer incentives to share. The first iteration of CC signals included attribution as a preference; today we believe that attribution must be a requirement. 

Our plan is to define best practices for attribution in AI contexts. AI developers often claim that attribution is simply not possible in LLMs. But this is a consequence of choices made during design, not a technical inevitability. We believe there is value in envisioning what attribution practices could look like in an AI ecosystem that prioritized them. And while there is no going back in time, we can demand attribution where it is technically possible within existing systems, such as Retrieval Augmented Generation (RAG), a method where AI systems pull from specific, traceable sources to generate responses. 

Our work will involve detailing ideal attribution guidance for AI systems, end users, and creators. We will then demonstrate how attribution can be realized in RAG models. This initiative serves two purposes: building shared understanding of what attribution in AI can and cannot currently achieve, and giving creators and AI users the tools to advocate for attribution as a baseline expectation. Strengthening attribution helps ensure that knowledge can circulate widely without losing connection to the people and communities who created it.

CC is looking to connect with experts working on attribution standards and developers working on AI systems that preserve attribution. If that describes your work, we would love to hear from you. 

Building New Tooling that Protects Public Interest Uses While Restoring Agency

Copyright alone cannot do this work. We believe maintaining a human-centered internet requires meaningful guardrails, upheld collectively. Our goal is to support an ecosystem that balances openness with agency, and access with accountability.

First, we are advocating for the development and usage of carefully scoped AI opt-outs that simultaneously sustain creator agency while protecting public interest uses. In an effort to address this need, we proposed additions to the IETF (the body that sets foundational internet standards) AI Preferences vocabulary that would help strike the right balance between creator agency and public interest reuse. It is essential that opt-out tooling and any related legislation protect public interest uses. This includes enabling cultural heritage institutions to preserve and analyze content, and supporting not-for-profit research and educational organizations in their work.

Second, we are doing research and development for a new tool designed to enable conditional access to openly shared collections and compilations. It will allow data stewards to set terms for accessing and using a collection or compilation that protect the sustainability of their technical infrastructure. These stewards may include libraries, archives, research institutions, data repositories, public knowledge projects, and cultural heritage organizations. Resource-heavy bulk reusers of data may be subject to more conditions, and public interest uses would be excluded entirely.

Without practical legal tools to define conditions for AI development, collections are left with blunt options: allow unrestricted extraction by AI developers, or restrict access entirely. Neither option reflects the goals of most knowledge stewards. This research and development is informed by close consultation with community members and stakeholders, such as dialogue with practitioners in the African context this past year, as well as broader explorations in the movement, such as this analysis on sharing of cultural heritage by Open Future Foundation, and the development of NOODL to rebalance power for marginalized language communities.

Many want to continue sharing their collections while ensuring that AI developers use them responsibly by respecting attribution, ensuring transparency, and meeting other safeguards aligned with their public interest missions. We want to build tooling to enable this in standardized, legally enforceable ways. 

What Happens Next

The exploration of these kinds of tools requires us to look beyond copyright alone, which is a real paradigm shift for CC, and not one we take lightly. We believe that investigating the risks and benefits of legal tools that support conditional access is an essential part of stewarding the long-term health of the commons. We need to preserve access to valuable knowledge resources while ensuring that the institutions and communities who steward them remain active participants in shaping the AI ecosystem.

Here is where things stand. This month, we are convening a workshop in London to begin working through the design and governance questions that new tooling raises. Later this year, we will be seeking pilot adopters to help us test and refine the approach in practice. We will share updates as this work develops. 

We have a clear plan, with these initiatives entering pilot phases within the year. Like many nonprofits, our ability to accelerate depends directly on the resources we have available. Support from our Open Infrastructure Circle has made progress to date possible, and as we mark our 25th anniversary, we have set a goal to raise $5 million to advance the next iteration of CC signals. If you are able, we invite you to support this work. 

Let’s collectively build what the commons needs next.

The post From Signals to Infrastructure: Strengthening the Commons for the AI Era appeared first on Creative Commons.

The biggest reason for the gap between updates is timing. We are deliberately resisting the pressure to move quickly simply because the broader technology landscape rewards speed. Our work touches the infrastructure of the commons. That requires care, consultation, and a willingness to sit with complexity.

So we slowed down. We let the first wave of AI development crest without rushing to respond. We took the time to understand where power is consolidating, where harms are emerging, and where meaningful intervention is actually possible. We are now at a point where we believe we can act in ways that will have real impact.

This post is meant to bring you into that journey. Our destination has not changed, but the path we are taking to get there has. Come along!

From Signals to Agency

When we first introduced CC signals, the idea was relatively straightforward. We proposed a set of preferences that creators could use to communicate with AI developers, relying on shared norms to guide behavior. It reflected how CC has historically operated. For 25 years, we have worked within copyright, building tools that expand access while maintaining a balance between creators and reusers. That history shaped our instincts. We assumed that a carefully calibrated, norms-based approach would move the ecosystem in a better direction.

But as we began consulting with our community, it became clear that this approach was not enough. The feedback was direct and consistent in stating that preference signals without enforcement do not meaningfully shift power. Signals alone cannot create agency in a system that many people did not choose to participate in. 

That feedback forced us to confront some of our own assumptions. For a long time, copyright has been our primary tool, and with good reason. CC licenses have enabled the sharing of tens of billions of works and have helped build a more open internet. But relying on copyright as the default lens for every problem has its limits, especially in an AI-mediated environment. 

Beyond Copyright

Over the past four months, we have been reexamining what it means to support the commons in this new context. 

CC licenses remain essential. They will continue to play a critical role in enabling human access to knowledge. However, when it comes to AI, copyright operates in a landscape that is uneven and often unclear. In many cases, CC license conditions do not apply to AI training. In others, they might. In some jurisdictions, broad exceptions mean that using CC-licensed works for AI development is lawful regardless of license conditions. At the same time, the presence of a CC license is often interpreted as permission to use the work in this way. That interpretation follows from how the licenses were designed; they grant broad permissions with limited conditions. 

The CC licenses were not designed with the scale and growing harms caused by the dominant, profit-driven approaches to AI in mind. And CC licenses do not capture the full range of intentions creators have in this AI-mediated world. Some creators are comfortable with their work being used in AI systems; others are not, and many fall somewhere in between.

Why New Tools Are Necessary

We also explored whether updating the CC licenses themselves, in the current paradigm, could provide a solution. Versioning has helped us adapt to new contexts before. But in this case, there are two novel factors at play.

The first is structural. CC licenses were designed not to enable control beyond copyright. They are intentionally scoped to copyright and related rights, and they explicitly do not allow additional restrictions that would limit uses outside that scope.

Our current trademark policy reinforces this. If restrictions are added that limit the permissions granted by a CC license, the work can no longer be presented as CC-licensed. This reflects the critical role that standardization has played in the success of open licensing. When you access a CC-licensed work, you should be able to rely on the terms and conditions written in the license to determine what your reuse obligations are. Expanding the CC license suite beyond its original focus on copyright would represent a significant change to how the licenses operate, and it could have unintended consequences on the existing license ecosystem.

This brings us to the second factor, which is that CC licensors have such a wide spectrum of needs and values about how and whether their works are used in AI. It is possible that new tooling would better address what may be irreconcilable within the open movement: some see any tool that attempts to control AI uses that fall outside of copyright as a betrayal; others see it as an imperative.

With the future of the commons in mind, at this time we believe that the best approach is to innovate with the development of new tools, where we can test and explore more freely. The CC licenses are one part of a larger strategy needed to meet this moment, which is evolving in an undefined legal landscape, just as it was 25 years ago when the CC licenses were first developed. 

The Stakes for the Commons

Our north star remains the same: sustain access to human knowledge. Today, that means more than enabling sharing. It means questioning long-held assumptions, and ensuring communities are in control of their own data. It means holding the tension that, in some cases, conditional access is better than no access. The commons needs guardrails in order to thrive. 

AI systems are being built on an unprecedented scale of knowledge extraction, drawing heavily from the commons. The governance systems that made open sharing possible have not kept pace with this shift. There are limited mechanisms for attribution in AI systems, few pathways for consent, and little transparency.

When the commons weakens, power over information becomes more concentrated. Knowledge moves into private datasets and proprietary systems controlled by a small number of actors. That limits who can access, verify, and build on information. Democracies depend on broad access to reliable knowledge. Public interest AI depends on diverse, high-quality data. 

A healthy commons is governed and sustained through systems that balance access with agency, openness with accountability. AI relies on the commons, not the other way around. If we want a future where knowledge is shared and where AI serves the public good, we need to ensure that the commons can thrive. This is the context in which we evolve CC signals. 

Strengthening CC Signals

Our problem statement has not changed, and neither has our end goal. But what we are building to get there has.

What began as a relatively narrow, tool-focused approach has evolved into something broader and more structural. CC signals is no longer limited to signaling preferences. CC signals is about addressing the underlying conditions that have made creator preferences so easy to ignore. This shift has led us toward work that is more ambitious, and necessarily more disruptive, in confronting the real harms to the commons emerging from dominant, profit-driven approaches to AI.

Check back with us next week, when we’ll share more about the specific interventions we are building from this foundation.

The post Update on CC Signals: What Changed and Why appeared first on Creative Commons.

What started as an organic exchange in various spaces has revealed something larger: a strong appetite to move these conversations into the open. At stake are not only questions about CC licenses but deeper issues of data sovereignty, equity, governance, and power in global knowledge systems. This blog post summarizes the themes emerging from those discussions and asks a broader question: how must “open” evolve to remain just, relevant, and community-centered?

A Shift 

CC licenses were designed to reduce friction in sharing knowledge. For many years, CC’s focus has been on legality, access, and reuse. By all accounts, we’ve been successful in meeting these goals and objectives. But in today’s digital and AI-driven landscape—particularly in the Global South—that framing is no longer sufficient.

Across the discussions, participants raised concerns that CC licenses, especially CC BY and CC0, are sometimes (inadvertently) enabling extractive practices. African language datasets, cultural knowledge, and community-generated data are increasingly being reused in ways that benefit global institutions and corporations, while the originating communities see little agency, recognition, or return. This governance and equity issue rightly challenges some long-held assumptions about openness. When data producers are required to share their data with a specific permissive license, it introduces a potential conflict between the requirement to share and whether that specific data should be shared at all.    

Key Challenges Identified

Colleagues highlighted the following challenges and concerns that are arising in their context and within their communities:

1. A perception gap around extractive use

CC licenses are often viewed as neutral tools, but in practice they can amplify existing power imbalances (as we know, infrastructure is not neutral!). For example, marginalized language and data communities may lack the leverage to negotiate how open resources are reused. Yes, open data can lead to communities having better access to information about where they live like air and water quality, but that same data can be used by large corporate entities to make decisions on where, for example, to build a new factory. 

2. Equity blind spots in traditional openness

In the context of the CC licenses, openness has historically been framed as a legal condition answering the question: can something be reused, modified, or shared? But we know that openness is much more than a set of legal tools; it is a set of values, a way of belonging, a wish for a better future. As large AI models continue to train on the billions of works and datasets made available via the CC licenses in the commons without giving back and while hoarding power, communities are responding by asking for openness that also accounts for agency, consent, reciprocity, and governance.

Data Governance and the Limits of One-Size-Fits-All Licensing

One of the most challenging threads in these discussions centers on data governance, particularly for African languages and community-curated datasets.

Several tensions stand out:

• Funders often mandate CC BY or CC0 for publicly funded research, leaving little room for community-specific governance models or the potential for a powerful interplay between CC licenses and community-created fit-for-purpose open licenses like NOODL. 
• CC licenses, by design, cannot prevent extractive reuse once content is made open.
• Local languages, cultural data, and community knowledge are not interchangeable with generic datasets—but licensing frameworks often treat them as such.

Openness is not binary, and context matters. Standardization matters and can amplify efforts to make knowledge accessible but only works when paired with governance. CC has worked with major funders of research to harmonize CC BY or CC0 across funders, but this work is built around the assumption that the license terms are adequate for all data and data distribution contexts. When there is no governance, what is the cost of harmonization? This community of researchers are asking whether CC can use its influence not only to promote CC licenses and legal tools but also to validate and support alternative, community-driven approaches where CC licenses fall short.

Open resources do not exist outside systems of power. Historically, openness has favored those with infrastructure, capital, and technical capacity—often institutions in the Global North. Simply making something open does not make it equitable, accessible, or just.

If the idealized version of openness has not delivered on its promise, is it time for CC to redefine it? What role can CC play in holding space, convening dissent, and legitimizing plural approaches to openness?

Where Do We Go From Here?

These conversations are not about arriving at neat conclusions. In fact, the goal is the opposite: to resist premature certainty and instead listen, reflect, and adapt.

For us as a community, this may mean:

• Being clearer about where CC licenses work and, just as importantly, where they don’t
• Acknowledging the limits of license-centric thinking
• Using the CC platform to amplify community-led definitions of openness
• Accepting that “the new open” may be more complex, more contextual, and intentionally less frictionless

The future of open knowledge depends on trust, dialogue, and shared governance. 

A special thank you to Vukosi Marivate, University of Pretoria; Chijioke Okorie, Data Science Law Lab, University of Pretoria; and Melissa Omino, CIPIT, Strathmore University; as well as members of the CC board of directors for convening these dialogues and sharing their perspectives with us at Creative Commons.

We want to know: Does this resonate with you? What are you seeing within your own context and community? We plan on continuing this dialogue throughout 2026 as we celebrate our 25th anniversary. What better time to reflect on our past contributions and challenge our thinking about the future. 

The post CC Licenses, Data Governance, and the African Context: Conversations and Perspectives appeared first on Creative Commons.

CC Signals: A Refresher

It is within this environment that we continue to develop CC signals. 

We introduced the CC signals concept last June during a live webinar, and further explored the motivation behind this work in our report From Human Content to Machine Data. We also shared the outcomes of our open feedback period following the CC signals kickoff. Since then, we’ve been experimenting in partnership with values-aligned stakeholders and developing pilot projects to test ideas raised by the community.

The goal of CC signals is to help creators and custodians of collections express how they want their content or data to be used in AI development in ways that uphold reciprocity, recognition, and sustainability. Today’s AI systems depend on vast amounts of human-created content, often collected without the awareness or involvement of those who made it. This has concentrated power and undermined trust in the social contract of the commons. 

CC signals responds by promoting community agency while preserving Creative Commons’ core commitment to access and openness. Ultimately, through CC signals and other interventions that infuse concepts of reciprocity in standards and practices, we envision an open internet where participation is equitable, creators are respected, and innovation advances the commons—not unchecked extraction.

CC Signals: Where Are We Now?

CC signals is an evolving, values-driven framework—currently being tested through a series of pilot efforts. Our strategy is to explore modular approaches across legal, technical, and normative dimensions to encourage responsible AI development practices. This allows CC signals to adapt as norms, technologies, and standards continue to evolve.  

At present, two key implementations are underway:

• Implementing CC signals on Mozilla Data Collective: We are working in partnership with our friends at Mozilla, looking at how implementation of CC signals would work on the Mozilla Data Collective platform, which is purpose-built to enable ethical dataset sharing and fair value exchange. Our plan is to test various ways of incorporating some measure of legal enforceability into CC signals. We also hope to use this as an opportunity to test which CC signal elements are most popular and impactful, and which ones have the biggest impact on AI developer behavior. 

• Adapting the CC signals contribution element in the RSL framework: Using the framework of the ecosystem contribution signal element, we are working with the RSL Collective to embed the notion of reciprocal contribution into this evolving standard. As a platform that will let rightsholders set machine-readable licensing terms for their content, integrating the contribution element ensures that standards such as RSL provide mechanisms for AI developers to contribute back to the commons at the collective or community level, not simply a one-to-one payment. 

Beyond CC signals itself, we are also exploring whether updates to CC’s license infrastructure could further strengthen and support the commons in the age of AI.  

Looking Ahead

We are actively seeking expressions of interest from dataset custodians who are interested in participating in the Mozilla Data Collective pilot project. If that’s you, we’d love to hear from you.  

We are also exploring sector-specific CC signals integrations, particularly within cultural heritage and science. 

Ultimately, CC signals are incarnations of what we want to see in the world—more recognition for authorship, sustainable commons communities, mutual commitments to shared resources. We are focused on building a vocabulary and vision for the values we think a successful commons needs to thrive. 

This work is resource-intensive. We need your support to ensure this work continues to be led by public interest organizations. Please donate today.

The post CC Signals: What We’ve Been Working On appeared first on Creative Commons.

Strange Bedfellows

That brings us to Really Simple Licensing (RSL). Publicly launched in September 2025, today the RSL Collective releases the RSL 1.0 standard. RSL is an open standard that lets publishers define machine-readable licensing terms for their content, including attribution, pay per crawl, and pay per inference compensation. This is an example of emerging technical systems used by websites to automate compensation for when their digital content—such as text, images, and structured data—is accessed by machines. We’ve been referring to these systems as pay-to-crawl. Think of it as the web’s attempt to answer the question: what tools are needed when bots become the biggest readers? If you are new to the concept, we recently published an issue brief that breaks it down in plain language.

On the surface, Creative Commons and pay-to-crawl systems are strange bedfellows. We have always been a champion of the open web and are concerned about a world where knowledge is harder to access. But we also recognize that responsible, interoperable systems can create leverage where none previously existed. Thoughtfully designed, pay-to-crawl systems may help curb extractive behavior by powerful actors while keeping the web open for everyone else.

Attribution + Compensation

In its early version 1.0 draft, RSL included attribution as one condition for machine access and reuse. From the standard: 

Attribution-Only License 

The publisher permits free reuse of the content on its site, provided that visible credit and a functional link to the original source are included. 

This is important as one example of more choices given to web publishers beyond the binary no access or all access. The inclusion of attribution also mirrors some elements of the proposed CC signal Credit. 

You must give appropriate credit based on the method, means, and context of your use.

Attribution + Reciprocity

But as the CC signals framework recognizes, attribution alone is not enough to address the very present power imbalances between AI developers and the commons. We need new tools that ensure the commons thrives and is sustained. 

We believe now is the time to act to infuse concepts of reciprocity in standards that are ready for adoption. That’s why we worked with the RSL Collective ahead of the release of version 1.0 to integrate a contribution component to the standard, which is described as:

A good faith monetary or in-kind contribution that supports the development or maintenance of the assets, or the broader content ecosystem. 

This is not about turning access into a tollbooth. It’s about acknowledging that extraction without reinvestment leads to collapse. There is a meaningful difference between paying a fee and giving back. One is transactional. The other is about responsibility.

When AI systems derive immense value from the digital commons, contribution isn’t compensation. It’s participation in the social contract that made that value possible in the first place.

Contribution could be in the form of:

• A donation back to a non-profit that stewards the dataset; 
• Support for the broader ecosystem that sustains the work;
• Openly licensing the model, or sharing a modified dataset back to the original steward;
• Or other models we haven’t yet imagined.

A Big Step: Many More to Come

The future of the web is being negotiated right now, in standards documents, in product decisions, and in design choices that shape how power flows online. Collaboration is vital if we’re going to achieve a systems-level response to rebalance power in the digital commons. 

There’s much more work to be done, particularly in developing what adherence to contribution means in different contexts. But we’re excited about where this is going. 

Our door is open. We welcome ideas, critiques, and collaboration. If you have ideas, consider engaging with us on LinkedIn or joining CC’s community platform on Zulip. 

Our year-end fundraising campaign is happening right now. While you are here, please consider making a donation to support this work.

The post Integrating Choices in Open Standards: CC Signals and the RSL Standard appeared first on Creative Commons.

Here at CC, we have the goal of defending and sustaining the digital commons in the face of developments in artificial intelligence.

We’ve recently introduced a new framework, CC signals, to offer a new way for stewards of large collections of content to indicate their preferences for how machines (and the humans controlling them) should contribute back to the commons.

As we develop our approach, we’re taking inspiration from the work of our partners, community, and other stakeholders. We’re particularly interested in efforts to understand:

• How AI scrapers are reshaping the web 
• Copyright, labor, surveillance, and resistance
• The effects of a new economy of data licensing
• Emerging ideas for more ethical AI and consensual data governance 

We’re reading (a lot!) on these topics, to help ensure that CC signals become part of a diverse set of solutions for protecting the commons in the unfolding AI future. Here’s some of the writing that’s shaping our thinking:

• Cloudflare launches a marketplace that lets websites charge AI bots for scraping – Maxwell Zeff, TechCrunch https://techcrunch.com/2025/07/01/cloudflare-launches-a-marketplace-that-lets-websites-charge-ai-bots-for-scraping/ 
• Legal frictions for data openness: Reflections from a case-study on re-use of the open web for AI training – Ramya Chandrasekhar https://hal.science/hal-05009616v1 
• AI Training, the Licensing Mirage, and Effective Alternatives to Support Creative Workers – Derek Slater, Tech Policy Press https://www.techpolicy.press/ai-training-the-licensing-mirage-and-effective-alternatives-to-support-creative-workers/ 
• “Wait, not like that”: Free and open access in the age of generative AI – Molly White, Citation Needed https://www.citationneeded.news/free-and-open-access-in-the-age-of-generative-ai/ 
• Telling AI to go away (but politely) – Nick Jackson, dxw https://www.dxw.com/2025/04/telling-ai-to-go-away-but-politely/ 
• Can AI Be Consentful? Rethinking Permission in the Age of Synthetic Everything – Giada Pistilli, Hugging Face https://huggingface.co/blog/giadap/consentful-ai 
• AI Should Help Fund Creative Labor – Mariana Mazzucato, Project Syndicate https://www.project-syndicate.org/onpoint/how-ai-profits-can-help-fund-cultural-production-by-mariana-mazzucato-and-fausto-gernone-2025-07 
• Licensing, Levies, and the Limits of Copyright – Paul Keller, OpenFuture https://openfuture.eu/blog/licensing-levies-and-the-limits-of-copyright/ 

We’d love for you to read and learn alongside us, share your thoughts, and contribute other articles and resources to this list! Connect with us on LinkedIn, Bluesky, or Mastodon. 

The post AI and the Commons: A Reading List appeared first on Creative Commons.

In June we kicked off a public feedback period on our proposal for CC signals. CC signals is a preference signals framework designed to sustain the commons and ensure the continued sharing of knowledge in the age of AI. 

The goal is to give holders of large datasets a way to set criteria for how their data may be used within AI training models. To give an example, a dataset holder may wish to require that any AI training that uses their data gives credit back to the original source (e.g. attribution), or that the resulting AI model is open. Like the CC licenses, CC signals builds on the idea of ‘some rights reserved’ and that creators and knowledge holders deserve meaningful choices in how their work is used. You can learn more on our website. 

Since our kickoff event, we have been listening closely to feedback. We heard from hundreds of creators, librarians, technologists, legal experts, and open advocates. We asked for feedback and you delivered! Your voices – supportive, skeptical, frustrated, or curious – are essential in shaping how CC signals develops. We’d like to summarize what we heard and how this feedback is being incorporated and addressed.

What We Heard

Across the conversations, several themes emerged: 

Concerns that CC is prioritizing AI companies over creators. A recurring concern is that CC signals seem to give legitimacy to AI training without doing enough to protect creators. 

Confusion and disagreement about the CC licenses and AI training. We heard frustration that the CC licenses are not being interpreted or enforced in ways that some creators expected. 

Strong calls for opt-outs. Many wondered why the draft CC signals did not include an opt-out option. 

Asking politely for AI developers to give back in exchange for datasets is not enough. We heard doubts that CC signals would work in practice, given the widespread evidence of AI companies ignoring copyright, licenses, and even technical protocols like robots.txt. 

Broader critique of AI’s role in society. There is a spectrum of views on AI across the CC community. Many of you stand firmly at the anti-AI end. For these voices, no technical framework, like CC signals, feels adequate without stronger laws and regulations. 

We haven’t been clear on who this tool is meant to serve and the use cases it is meant to address. Naturally, the needs of an individual creator, like an artist, are quite different from those operating at an institutional or collective level. We heard loud and clear that CC signals, as currently conceived, does not meet the diverse needs of individual creators.

Requests for clarity. Many asked for more details about implementation and interoperability, including our long-term vision for CC signals as part of our broader mission. 

We understand how deeply personal these issues are for many of you, especially artists and creators who feel their work is being taken without consent and are looking for ways to fight back. That frustration is real, and we take it seriously. 

What We’re Doing Next

Improving clarity around CC’s position. We know many of you are worried that CC has “taken sides” or is being influenced by AI companies. We want to be clear: the driving motivation of CC signals is to defend and sustain the commons by developing practical tools for knowledge holders. Going forward, we will aim to clarify our guiding principles and positions in ways that translate to product decisions. 

Strengthening messaging and education. We are committed to expanding resources on how the CC licenses and CC signals could interact, examples of how signals could work in practice, and deeper dives into questions of copyright within the AI landscape. If you haven’t already, take a look at our legal primer on understanding the CC licenses and AI training. The better informed the CC community is about AI and the commons at large, the more effective we can be as a community to defend the commons. 

Clarifying the use cases for CC signals. This phase of CC signals is designed to serve large and open dataset holders, not the individual creator. Your feedback helped us recognize that this focus was not easy to square with our decision to leverage technical protocols used by anyone with a website. As a result, the target audience for CC signals was not clear. As we decide on next steps in product development, we plan to focus on specific use cases to put our goals and objectives into practice. 

Deepening global engagement and inviting stakeholders into product development. We plan to continue conversations with diverse audiences to inform the future of CC signals through an iterative process. The rest of this year will be focused on exploring and testing possible integrations of CC signals with pilot adopters. From this, we hope to extrapolate findings as we explore wider adoption of CC signals in the future. 

Maintaining transparency in development. Our GitHub repository will stay open and up to date. We are creating a roadmap that will be shared publicly and will provide consistent updates (either on the blog or via a virtual town hall) on our progress. This feedback loop is not over; it will be built into how CC signals will evolve. 

Looking Ahead

The future of the commons depends on tools that reflect shared values of openness, fairness, and agency. We know many of you remain skeptical. 

CC signals is not final. It is an experiment in building a new layer of choice in an age where the rules are rapidly shifting. We will keep listening, adjusting, and collaborating until we arrive at something that genuinely serves the commons.

Thank you to everyone who took the time to write, question, challenge, and support us. Please stay engaged. Together, we can ensure that Creative Commons continues to stand where it always has: with the community, for the commons.

The post We Asked, You Answered: How Your Feedback Shapes CC Signals appeared first on Creative Commons.

Thanks to everyone who attended our CC signals project kickoff last week. We’re receiving plenty of feedback, and we appreciate the insights. We are listening to all of it and hope that you continue to engage with us as we seek to make this framework fit for purpose.

Some of the input focuses on the specifics of the CC signals proposal, offering constructive questions and suggesting ideas for improving CC signals in practice. The most salient type of feedback, however, is touching on something far deeper than the CC signals themselves – the fact that so much about AI seems to be happening to us all, rather than with or for us all, and that the expectations of creators and communities are at risk of being overshadowed by powerful interests.

This sentiment is not a surprise to us. We feel it, too. In fact, it is why we are doing this project. CC’s goal has always been to grow and sustain the thriving commons of knowledge and culture. We want people to be able to share with and learn from each other, without being or feeling exploited. CC signals is an extension of that mission in this evolving AI landscape.

We believe that the current practices of AI companies pose a threat to the future of the commons. Many creators and knowledge communities are feeling betrayed by how AI is being developed and deployed. The result is that people are understandably turning to enclosure. Eventually, we fear that people will no longer want to share publicly at all. 

CC signals are a first step to reduce this damage by giving more agency to those who create and hold content. Unlike the CC licenses, they are explicitly designed to signal expectations even where copyright law is silent or unclear, when it does not apply, and where it varies by jurisdiction. We have listened to creators who want to share their work but also have concerns about exploitation. CC signals provide a way for creators to express those nuances.  The CC signals build on top of developing standards for expressing AI usage preferences (e.g., via robots.txt). Creators who want to fully opt out of machine reuse do not need to use a CC signal. CC signals are for those who want to keep sharing, but with some terms attached.

The challenge we’re all facing in this age of AI is how to protect the integrity and vitality of the commons. The listening we’ve been doing so far, across creator communities and open knowledge networks, has led us here, to CC signals. Our shared commitment is to protect the commons so that it remains a space for human creativity, collaboration, and innovation, and to make clear our expectation that those who draw from it give something in return. 

Our goal is to advocate for reciprocity while upholding our values that knowledge and creativity should not be treated as commodities. 

Our goal is to find a path between a free-for-all and an internet of paywalls.

Copyright will not get us there. Nor should it. And we don’t think the boundaries of copyright tell us everything we need to know about navigating this moment. Just this week, Open Future released a report that calls for going beyond copyright in this debate, on the path to a healthy knowledge commons.

This is the beginning of the conversation, not the end. We are listening. From what we have heard, CC signals, or something like it, is the best practical mechanism to avoid the dual traps of total exploitation or total enclosure, both of which damage the commons. We have shared our current progress because we want to learn how to design it to meet your needs. We invite you to continue sharing feedback so we can shape CC signals together in a way that works for diverse communities.

In the months ahead, we’ll be providing more detail about how CC signals are developing, including key themes we are hearing, along with the questions we are exploring and our next steps.

The post Why CC Signals: An Update appeared first on Creative Commons.

Creative Commons (CC) today announces the public kickoff of the CC signals project, a new preference signals framework designed to increase reciprocity and sustain a creative commons in the age of AI. The development of CC signals represents a major step forward in building a more equitable, sustainable AI ecosystem rooted in shared benefits. This step is the culmination of years of consultation and analysis. As we enter this new phase of work, we are actively seeking input from the public. 

As artificial intelligence (AI) transforms how knowledge is created, shared, and reused, we are at a fork in the road that will define the future of access to knowledge and shared creativity. One path leads to data extraction and the erosion of openness; the other leads to a walled-off internet guarded by paywalls. CC signals offer another way, grounded in the nuanced values of the commons expressed by the collective.

Based on the same principles that gave rise to the CC licenses and tens of billions of works openly licensed online, CC signals will allow dataset holders to signal their preferences for how their content can be reused by machines based on a set of limited but meaningful options shaped in the public interest. They are both a technical and legal tool and a social proposition: a call for a new pact between those who share data and those who use it to train AI models.

“CC signals are designed to sustain the commons in the age of AI,” said Anna Tumadóttir, CEO, Creative Commons. “Just as the CC licenses helped build the open web, we believe CC signals will help shape an open AI ecosystem grounded in reciprocity.”

CC signals recognize that change requires systems-level coordination. They are tools that will be built for machine and human readability, and are flexible across legal, technical, and normative contexts. However, at their core CC signals are anchored in mobilizing the power of the collective. While CC signals may range in enforceability, legally binding in some cases and normative in others, their application will always carry ethical weight that says we give, we take, we give again, and we are all in this together. 

“If we are committed to a future where knowledge remains open, we need to collectively insist on a new kind of give-and-take,” said Sarah Hinchliff Pearson, General Counsel, Creative Commons. “A single preference, uniquely expressed, is inconsequential in the machine age. But together, we can demand a different way.”

Now Ready for Feedback 

More information about CC signals and early design decisions are available on the CC website. We are committed to developing CC signals transparently and alongside our partners and community. We are actively seeking public feedback and input over the next few months as we work toward an alpha launch in November 2025. 

Get Involved

Join the discussion & share your feedback

To give feedback on the current CC signals proposal, hop over to the CC signals GitHub repository. You can engage in a few ways: 

1. Read about the technical implementation of CC signals
2. Join the discussion to share feedback about the CC signals project
3. Submit an issue for any suggested direct edits

Attend a CC signals town hall

We invite our community to join us for a brief explanation of the CC signals framework, and then we will open the floor to you to share feedback and ask questions. 

Tuesday, July 15
6–7 PM UTC
Register here.

Tuesday, July 29
1–2 PM UTC
Register here.

Friday, Aug 15
3–4 PM UTC
Register here. 

Support the movement

CC is a nonprofit. Help us build CC signals with a donation. 

The age of AI demands new tools, new norms, and new forms of cooperation. With CC signals, we’re building a future where shared knowledge continues to thrive. Join us.

The post Introducing CC Signals: A New Social Contract for the Age of AI appeared first on Creative Commons.

Creative Commons training efforts strengthen our mission to “empower individuals and communities around the world through technical, legal, and policy solutions that enable the sharing of education, culture, and science in the public interest.” In 2024, our Learning & Training team focused on: 1) piloting new partnerships, 2) expanding training options, and 3) reaching new communities.  We are pleased that our 2024 training and engagement efforts supported national governments, universities, secondary education institutions, NGOs, librarians, cultural heritage professionals, and web developers spanning almost every continent.  See below for highlights, and contact us if you would like to collaborate in 2025. 

• Piloting new partnerships:
  •  CC worked in partnership with the University of Nebraska at Omaha on “Introduction to Open Educational Resources,” our first professional development microcredential course. 
  • We partnered with Library Juice Academy to create an additional microcredential course launching in January 2025. Registration is currently open until all seats are filled. 
  • Similar to our partnership with the US Midwest Higher Education Compact (MHEC), we partnered with BCcampus to support subsidized CC Certificate training for additional higher education institutions. 
• Expanding training options: 
  • CC provided increased support for our CC Certificate program and its 1,892 alumni across 68 countries. The CC Certificate program trains advocates, educators, librarians, and cultural heritage professionals in copyright, open licensing and open practices for advocacy. This year, CC awarded 35 scholarships for Certificate courses; hosted 24 webinars covering topics such as Digitized Representation by Heritage Institutions, Accessible OER, AI, copyright, and CC licenses; and provided multiple avenues of engagement for Certificate alumni, including both formal and informal mentorship options and a monthly newsletter.
  • CC increased our customized training offerings, providing workshops, tutorials, talks and training for: the AAC&U’s Institute on OER, Africa Journals Online, CEU Press and Ukrainian Partners, California Zero Textbook Cost (ZTC) program grantees, CC Rwanda, the Connecticut Museum of Culture and History, Ghanaian secondary school educators focused on Open Education For Climate and Environmental Sustainability, ISTE Live 2024, the Missouri’s A&OER Conference pre-conference training day, OER and Social Justice for Affordable Learning Kentucky, Utrecht University, Wikimania, in partnership with Ukrainian and Netherlands librarians, a Willoughby Institute faculty workshop at Dickinson College, and a WordPress Campus Conference Lightning Talk. See additional engagements including keynotes and moderated discussions in our CC Open Education Year in Review.
• Reaching new communities:
  •  To reach more librarians, CC collaborated with the American Library Association to provide a 4-week webinar series for ALA’s Core division, supporting librarians’ professional development. CC also hosted presentations at ALA LibLearnX and ALA Annual. 
  • To reach more communities involved in Open Science and Open Data, CC hosted trainings including: an Open Climate Data Recommendations and CC Licensing 101 workshop for Open Education week, Open Data training for the California Department  of Transportation, an open data workshop for ocean and climate researchers in partnership with Intertidal Agency, the Michael J. Fox Foundation, and the Estonian Ministry of Economic Affairs and Communications. 
  • CC provided training for additional government audiences with a CC 101 for the U.S. Department of State’s Bureau of Educational and Cultural Affairs.

Reflecting on 2024, we are grateful for the friendships and collaborations forged, and the new communities we had the pleasure of meeting. As we continue working toward the three goals in 2025, we hope to connect! If you would like to partner with CC, host a CC training for your institution, or get CC support for your community of practice, please let us know. Learn more on our website and email learning [at] creativecommons.org for more information. We’d be delighted to help you continue to grow your knowledge expertise in opening access to research, science, education, and culture.

The post CC Learning and Training: 2024 Year in Review appeared first on Creative Commons.