We recently shared an update on the evolution of CC signals. As AI systems increasingly extract value from the commons without adequate consent, attribution, or transparency, sustaining a healthy commons requires stronger governance and accountability. This reflects a shift in our approach: from expressing preferences to rebalancing power to protect the commons.
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.
- Helping People Make Informed Decisions in the Current Moment
- Making Attribution the Norm in AI
- 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.
Posted 13 May 2026