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.

The Creative Commons (CC) Certificate courses are widely considered an essential resource for open access education and for increasing capacity for individuals and institutions using the CC licenses to increase open access.  

The CC Certificate program offers in-depth courses about CC licenses, open practices, and the ethos of the Commons. Courses are composed of various readings, quizzes, discussions, and practical exercises to develop learners’ open skills. Currently we offer a CC Certificate for Open Culture, a CC Certificate for Academic Librarians, and a CC Certificate for Educators. Courses are open to everyone, from university students and entry-level professionals to experts in the fields of library science, education, and cultural heritage. 

With the goal of reducing the barrier of participating in one of these essential trainings, CC is proud to have recently awarded eight scholarships. These scholarships would not be possible without your donations. We invite you to donate today so that we can continue offering these scholarships.  You may also want to consider joining our Open Infrastructure Circle so that we can increase participation in these trainings globally. 

Join us in congratulating the following scholarship recipients and keep reading to learn more about their journey in the open community: 

ABIR Mohammed Galib Hasan, Bangladesh

Galib is a PhD Researcher in Hokkaido University, Japan. His primary research areas are: Educational Technology, Open Education and Generative AI. He was a founding member of the CC Bangladesh Chapter, serving as the Education Lead since 2018. Galib also served on the program committee of CC Global Summit in 2019 and 2020.

Bukola James, Nigeria

Bukola James is a certified librarian, Wikimedian, and community coordinator for the African Wikipedian Alliance. She also serves as the co-lead for the Open Culture Platform’s Outreach Working Group and as a Sub-Saharan Liaison Officer for the Wikimedia Foundation Peer Learning Program. Additionally, Bukola is a  communications expert for the EduWiki Newsletter and a special adviser for the EduWiki User Group. She holds the position of co-team and project lead within African Wiki Women and other impactful initiatives.

Chaidir Amir, Indonesia

Chaidir is a professional librarian who has been working in libraries since 2023. He has certifications and competence in library management based on information and communication technology. He is an active member of multiple library forums and associations. Chaidir also serves as an accreditation assessor and library training facilitator.

Jes Graham, South Africa

Jes is a 28-year-old, disabled, non-binary South African who works at the University of Cape Town in open education, specifically in the development and production of open textbooks. Their driving motto for their work is to “be conscientiously creative in the pursuit of developing and sharing accessible knowledge through design.” To this end, Jes combines their skills in graphic design, Disability Studies, and editorial work and publishing to develop open educational resources (with a strong focus on multiple forms of accessibility) from a South African perspective. In their current work at the University of Cape Town, Jes has developed foundational skills in CC licensing, but aims to advance this knowledge to more deeply integrate CC licensing in their own work and support others in the local design and academic community.

John Okewole, Nigeria

John is an open education advocate working locally by encouraging colleagues to engage openness as a culture and attitude, and globally as a CC Global Network member and member of CC’s Open Education Platform). Some of his recent contributions include acting as a member of the Working Group 4 — Beyond Copyright: the Ethics of Open Sharing and serving as a co-lead of the CC Open Education Platform’s working group on the UNESCO Recommendation on OER. John is a Commonwealth Scholar who has completed an MA in Online and Distance Education at the Open University, UK and he also has a certificate in Designing and Facilitating E-Learning (Level 5) at the Open Polytechnic of New Zealand.

Jonas Bäckelin, Sweden

Jonas is currently the Content Manager on the Creative Commons Sverige team and the Sweden Chapter Lead. Outside of his CC work, Jonas is the solution manager and learning designer at Adda Kompetens, a part of the Swedish Association of Local Authorities and Regions (SALAR). He also serves as the moderator of the Upskilling och Reskilling committee at Swedish JobTech.

Tina Kalan, Slovenija

After working in the public school system and the national library, Tina has found her place in the academic library world. Her work is very dynamic, including everything from cataloging to information literacy courses. An important part of her workload is bibliographies, and her goal is to provide support to patrons, from students to researchers, in questions related to open access, open science and research assessments.

Tri Astari, Indonesia

Tri Astari is a lecturer who creates educational content under CC licenses, driven by a strong desire to make knowledge easily accessible. In addition, she is a member of Wikimedia Indonesia.

Congratulations again to the recipients. If you are interested in the CC Certificate courses, we invite you to register for 2025

The post Meet the Recipients of the Fall 2024 CC Certificate Scholarship appeared first on Creative Commons.

We’re proud to announce Creative Commons’ Legal Tools have been reviewed and accepted into the Digital Public Goods Alliance (DPGA) DPG Registry. The DPGA is a multi-stakeholder initiative, endorsed by the United Nations Secretary-General, that is working to accelerate the attainment of the UN Sustainable Development Goals in low- and middle-income countries. DPGA does this by facilitating the discovery, development, use of, and investment in digital public goods (DPGs) in order to create a more equitable world.

Being recognized as a DPG increases the visibility, support for, and prominence of open projects that have the potential to tackle global challenges. To become a digital public good, all projects are required to meet the DPG Standard to ensure that projects truly encapsulate open source principles. 

Creative Commons provides and stewards the CC licenses and public domain tools that give every person and organization in the world a free, simple, and standardized way to grant copyright permissions for creative and academic works. In addition, the licenses support proper attribution and enable others to copy, distribute, and make use of those works. CC legal tools are digital public infrastructure that make the legal sharing of DPGs possible. 

At Creative Commons, we are thrilled to have our Legal Tools recognised as DPGs as they can empower people to dramatically improve access to open content. By advocating for the use and implementation of DPGs, global communities can work together in prioritizing and mobilizing resources to help solve global challenges. CC’s legal tools and our programs play a critical role in helping to advance the DPG ecosystem.

For any inquiries about CC’s involvement in the Digital Public Goods Alliance, please reach out to Cable Green. For more information on the Digital Public Goods Alliance please reach out to hello@digitalpublicgoods.net.

Join us by supporting this ongoing work. You have the power to make a difference in a way that suits you best. By donating to CC, you are not only helping us continue our vital work, but you also benefit from tax-deductible contributions. Making your gift is simple – just click here. Thank you for your support.

The post CC Legal Tools Recognized as Digital Public Goods appeared first on Creative Commons.

At the intersection of rapid advancements in generative AI and our ongoing strategy refresh, we’ve been deeply engaged in researching, analyzing, and fostering conversations about AI and value alignment. Our goal is to ensure that our legal and technical infrastructure remains robust and suitable in this rapidly evolving landscape.

In these uncertain times, one thing is clear: there is an urgent need to develop new, nuanced approaches to digital sharing. This is Creative Commons’ speciality and we’re ready to take on this challenge by exploring a possible intervention in the AI space: preference signals. 

Understanding Preference Signals

We’ve previously discussed preference signals, but let’s revisit this concept. Preference signals would empower creators to indicate the terms by which their work can or cannot be used for AI training. Preference signals would represent a range of creator preferences, all rooted in the shared values that inspired the Creative Commons (CC) licenses. At the moment, preference signals are not meant to be  legally enforceable. Instead, they aim to define a new vocabulary and establish new norms for sharing and reuse in the world of generative AI.

For instance, a preference signal might be “Don’t train,” “Train, but disclose that you trained on my content,” or even “Train, only if using renewable energy sources.”

Why Do We Need New Tools for Expressing Creator Preferences?

Empowering creators to be able to signal how they wish their content to be used to train generative AI models is crucial for several reasons:

• The use of openly available content within generative AI models may not necessarily be consistent with creators’ intention in openly sharing, especially when that sharing took place before the public launch and proliferation of generative AI. 
• With generative AI, unanticipated uses of creator content are happening at scale, by a handful of powerful commercial players concentrated in a very small part of the world.
• Copyright is likely not the right framework for defining the rules of this newly formed ecosystem. As the CC licenses exist within the framework of copyright, they are also not the correct tools to prevent or limit uses of content to train generative AI. We also believe that a binary opt-in or opt-out system of contributing content to AI models is not nuanced enough to represent the spectrum of choice a creator may wish to exercise.  

We’re in the research phase of exploring what a system of preference signals could look like and over the next several months, we’ll be hosting more roundtables and workshops to discuss and get feedback from a range of stakeholders. In June, we took a big step forward by organizing our most focused and dedicated conversation about preference signals in New York City, hosted by the Engelberg Center at NYU.

Six Highlights from Our NYC Workshop on Preference Signals

• Creative Commons as a Movement

Creative Commons is a global movement, making us uniquely positioned to tackle what sharing means in the context of generative AI. We understand the importance of stewarding the commons and the balance between human creation and public sharing. 

• Defining a New Social Contract

Designing tools for sharing in an AI-driven era involves collectively defining a new social contract for the digital commons. This process is essential for maintaining a healthy and collaborative community. Just as the CC licenses gave options for creators beyond no rights reserved and all rights reserved, preference signals have the potential to define a spectrum of sharing preferences in the context of AI that goes beyond the binary options of opt-in or opt-out. 

• Communicating Values and Consent

Should preference signals communicate individual values and principles such as equity and fairness? Adding content to the commons with a CC license is an act of communicating values;  should preference signals do the same? Workshop participants emphasized the need for mechanisms that support informed consent by both the creator and user.

• Supporting Creators and Strengthening the Commons

The most obvious and prevalent use case for preference signals is to limit use of content within generative AI models to protect artists and creators. There is also the paradox that users may want to benefit from more relaxed creator preferences than they are willing to grant to other users when it comes to their content. We believe that preference signals that meet the sector-specific needs of creators and users, as well as social and community-driven norms that continue to strengthen the commons, are not mutually exclusive. 

• Tagging AI-Generated vs. Human-Created Content

While tags for AI-generated content are becoming common, what about tags for human-created content? The general goal of preference signals should be to foster the commons and encourage more human creativity and sharing.  For many, discussions about AI are inherently discussions about labor issues and a risk of exploitation. At this time, the law has no concept of “lovingly human”,  since humanness has been taken for granted until now. Is “lovingly human” the new “non-commercial”? Generative AI models also force us to consider what it means to be a creator, especially as most digital creative tools will soon be driven by AI. Is there a specific set of activities that need to be protected in the process of creating and sharing? How do we address human and generative AI collaboration inputs and outputs? 

• Prioritizing AI for the Public Good

We must ensure that AI benefits everyone. Increased public investment and participatory governance of AI are vital. Large commercial entities should provide a public benefit in exchange for using creator content for training purposes. We cannot rely on commercial players to set forth industry norms that influence the future of the open commons. 

Next Steps

Moving forward, our success will depend on expanded and representative community consultations. Over the coming months, we will:

• Continue to convene our community members globally to gather input in this rapidly developing area;
• Continue to consult with legal and technical experts to consider feasible approaches;
• Actively engage with the interconnected initiatives of other civil society organizations whose priorities are aligned with ours;
• Define the use cases for which a preference signals framework would be most effective;
• Prototype openly and transparently, seeking feedback and input along the way to shape what the framework could look like;
• Build and strengthen the partnerships best suited to help us carry this work forward.

These high-level steps are just the beginning. Our hope is to be piloting a framework within the next year. Watch this space as we explore and share more details and plans. We’re grateful to Morrison Foerster for providing support for the workshop in New York.

Join us by supporting this ongoing work

You have the power to make a difference in a way that suits you best. By donating to CC, you are not only helping us continue our vital work, but you also benefit from tax-deductible contributions. Making your gift is simple – just click here. Thank you for your support.

The post Six Insights on Preference Signals for AI Training appeared first on Creative Commons.

The intersection of AI, copyright, creativity, and the commons has been a focal point of conversations within our community for the past couple of years. We’ve hosted intimate roundtables, organized workshops at conferences, and run public events, digging into the challenging topics of credit, consent, compensation, transparency, and beyond. All the while, we’ve been asking ourselves:  what can we do to foster a vibrant and healthy commons in the face of rapid technological development? And how can we ensure that creators and knowledge-producing communities still have agency?

History and Evolution

When Creative Commons was founded over 20 years ago, sharing on the internet was broken. With the introduction of the CC licenses, the commons flourished. Licenses that enabled open sharing were perfectly aligned with the ideals of giving creators a choice over how their works were used.

Those who embrace openly sharing their work have a myriad of motivations for doing so. Most could not have anticipated how their works might one day be used by machines: to solve complex medical questions, to create other-wordly pictures of dogs, to train facial recognition systems – the list goes on.

Can we continue to foster a vibrant and healthy commons in today’s technological environment? How can we think innovatively about creator choice in this context?

Preference Signals

Preference signals for AI are the idea that an agent (creator, rightsholder, entity of some kind) is able to signal their preference with regards to how their work is used to train AI models. Last year, we started thinking more about this concept, as did many in the responsible tech ecosystem. But to date the dialog is still fairly binary, offering only all-or-nothing choices, with no imagination for how creators or communities might want their work to be used.

Enabling Commons-Based Participation in Generative AI

What was once a world of creators making art and researchers furthering knowledge, has the risk of being reduced to a world of rightsholders owning, controlling, and commercializing data. In this bleak future, it’s no longer a photo album, a poetry book, or a family blog. It’s content, it’s data, and eventually, it’s tokens.

We recognize that there is a perceived tension between openness and creator choice. Namely, if we  give creators choice over how to manage their works in the face of generative AI, we may run the risk of shrinking the commons. To potentially overcome, or at least better understand the effect of generative AI on the commons, we believe  that finding a way for creators to indicate “no, unless…” would be positive for the commons. Our consultations over the course of the last two years have confirmed that:

• Folks want more choice over how their work is used.
• If they have no choice, they might not share their work at all (under a CC license or strict copyright).

If these views are as wide ranging as we perceive, we feel it is imperative that we explore an intervention, and bring far more nuance into how this ecosystem works.

Generative AI is here to stay, and we’d like to do what we can to ensure it benefits the public interest. We are well-positioned with the experience, expertise, and tools to investigate the potential of preference signals.

Our starting point is to identify what types of preference signals might be useful. How do these vary or overlap in the cultural heritage, journalism, research, and education sectors? How do needs vary by region? We’ll also explore exactly how we might structure a preference signal framework so it’s useful and respected, asking, too: does it have to be legally enforceable, or is the power of social norms enough?

Research matters. It takes time, effort, and most importantly, people. We’ll need help as we do this. We’re seeking support from funders to move this work forward. We also look forward to continuing to engage our community in this process. More to come soon.

The post Questions for Consideration on AI & the Commons appeared first on Creative Commons.

A key question facing Creative Commons as an organization, and the open movement in general, is how we will respond to the challenge of shaping artificial intelligence (AI) towards the public interest, growing and sustaining a thriving commons of shared knowledge and culture.

So much of generative AI is built on the digital infrastructure of the commons and uses the vast quantity of images, text, video, and rich data resources of the internet. Organizations train their models with trillions of tokens from publicly available datasets like CommonCrawl, GitHub open source projects, Wikipedia, and ArXiV.

Access to the commons has enabled incredible innovations while creating the conditions for the concentration of power in entities that are able to amass the immense energy and data needed to train AI models. Community consultations at conferences like MozFest, RightsCon, Wikimania, and the CC Global Summit have also revealed concerns about transparency, bias, fairness, and attribution in AI.

Alignment Assembly

To start addressing some of these challenges, between 13 February and 15 March, Open Future will host an asynchronous, virtual alignment assembly for the open movement to explore principles and considerations for regulating generative AI. We hope to reach participants spread across different fields of open and coming from different regions of the world. We are organizing the assembly in partnership with Open Future and Fundación Karisma.

We want to bring to the conversation the perspectives of:

• Activists and experts, including digital rights advocates and legal experts
• Stewards: people from organizations that steward collections that are part of the digital commons such as Wikimedia, open access repositories, and cultural heritage collections
• Creators: people who create works that form part of the digital commons, broadly: not only visual artists and musicians but also researchers who do open science or open source programmers

We will use the process of an alignment assembly, an experiment in collective deliberation and decision-making. This model is pioneered by the Collective Intelligence Project (CIP), led by Divya Siddarth and Saffron Huang. The model has been used by OpenAI, Anthropic, and the government of Taiwan.

You can sign up to take part in the process by registering your interest here (we will only use the contact information to invite you to the assembly and to provide updates and delete it once the assembly process is complete).

Background

Creative Commons has long been considering the intersection of copyright and AI. CC submitted comments to the World Intellectual Property Organization’s consultations on copyright and AI in 2020. When considering usage of CC-licensed work in AI, the organization explored in 2021 “Should CC-licensed work be used to train AI”. More recently, CC carried out consultations at MozFest, RightsCon, Wikimania, and the CC Global Summit, while publishing ongoing analysis of the AI landscape.

Ahead of the Creative Commons Global Summit last year, Creative Commons and Open Future hosted a workshop on generative AI and its impact on the commons. The group agreed and released a set of principles on “Making AI work for Creators and the Commons.” Now, we would like to test and expand this work. 

Outcome

The Alignment Assembly on AI and the Commons builds on and continues all of this work.

We treat the principles as a starting point. We are using the alignment assembly methodology and the pol.is tool to understand where there is consensus and which principles generate controversy. In particular, how much alignment there is between the perspectives of activists, creators, and stewards of the commons.

At the end of the process, we will produce a report with the outcomes of the assembly and a proposal for a refined set of principles. As the policy debate about the commons and AI develops, we hope the assembly will provide insights into better regulation of generative AI.

Sign up here to share your thoughts on regulating generative AI.

The post An Invitation for Creators, Activists, and Stewards of the Open Movement appeared first on Creative Commons.

Since our founding, we have sought out ways that new technologies can serve the public good, and we believe that generative AI can be a powerful tool to enhance human creativity and to benefit the commons. At the same time, we also recognize that it carries with it the risk of bringing about significant harm. We used this opportunity to explain to the Copyright Office why we believe that the proper application of copyright law can guide the development and use of generative AI in ways that serve the public and to highlight what we have learned from our community through the consultations we have held throughout 2023 and at our recent Global Summit about both the risks and opportunities that generative AI holds.

In this post we summarize the key point of our submission, namely:

• AI training generally constitutes fair use
• Copyright should protect AI outputs with significant human creative input
• The substantial standard similarity should apply to Infringement by AI outputs
• Creators should be able to express their preferences
• Copyright cannot solve everything related to generative AI

AI training generally constitutes fair use

We believe that, in general, training generative AI constitutes fair use under current U.S. law. Using creative works to train generative AI fits with the long line of cases that has found that non-consumptive, technological uses of creative works in ways that are unrelated to the expressive content of those works are transformative fair uses, such as Authors Guild v. Google and Kelly v. Arriba Soft. Moreover, the most recent Supreme Court ruling on fair use, Andy Warhol Foundation v. Goldsmith, supports this conclusion. As we commented upon the decision’s release, the Warhol case focus on the specific way a follow-on use compares with the original use of a work indicates that training generative AI on creative works is transformative and should be fair use. This is because the use of copyrighted works for AI training has a fundamentally different purpose from the original aesthetic purposes of those works.

Copyright protection for AI outputs subject to significant human creative input

We believe that creative works produced with the assistance of generative AI tools should only be eligible for protection where they contain a significant enough degree of human creative input to justify protection, just like when creators use any other mechanical tools in the production of their works. The Supreme Court considered the relationship between artists and their tools vis-a-vis copyright over 100 years ago in Burrow-Giles v. Sarony, holding that copyright protects the creativity that human artists’ incorporate into their works, not the work of machines. While determining which parts of a work are authored by a human when using generative AI will not always be clear, this issue is not fundamentally different from any other situation where we have to determine the authorship of individual parts of works that are created without AI assistance.

Additionally, we believe that developers of generative AI tools should not receive copyright protection over the outputs of those tools. Copyright law already provides enough incentives to encourage development of these tools by protecting code, and extending protection to their outputs is unnecessary to encourage innovation and investment in this space.

Infringement should be determined using the substantial similarity test

We believe that the substantial similarity standard that already exists in copyright law is sufficient to address where AI outputs infringe on other works. The debate about how copyright should apply to generative AI has often been cast in all-or-nothing terms — does something infringe on pre-existing copyrights or not? The answer to this question is certainly that generative AI can infringe on other works, but just as easily it may not. As with any other question about the substantial similarity between two works, these issues will be highly fact specific, and we cannot automatically say whether works produced by generative AI tools infringe or not.

Creators should be able to express their preferences

In general, we believe there is value in methods that enable individuals to to signal their preferences for how their works are shared in the context of generative AI. In our community consultations, we heard general support for preference signals, but there was no consensus in how best to do this. Opt-ins and opt-outs may be one way, but we do not believe they need to be required by US copyright law; instead, we would like to see voluntary schemes, similar to approaches to web scraping, which allow for standardized expression of these preferences without creating strict barriers to usage in cases where it may be appropriate.

Transparency is necessary to build trust — Copyright is only one lens through which to consider AI regulation

We urge caution and flexibility in any approach to regulating generative AI through copyright. We believe that copyright policy can guide the development of generative AI in ways that benefit all, but that overregulation or inappropriate regulation can hurt both the technology and the public. For example, measures that improve transparency into AI models can build trust in AI models by allowing outside observers to “look under the hood” to investigate how they work. But these measures should not be rooted in copyright law. Copyright is just one lens through which we can view generative AI, and it is ill equipped to deal with many of the social harms that concern us and many others. Attempting to use copyright to solve all of these issues may have unintended consequences and ultimately do more harm than good.

We are happy to see the Copyright Office seeking out guidance on these many difficult questions. We will have to wait to see what comes from this, but we will hope for the best, and continue to engage our community so we can more fully understand what role generative AI should play in building the commons and serving the public good.

Read CC’s full submission to the Copyright Office >

The post CC Responds to the United States Copyright Office Notice of Inquiry on Copyright and Artificial Intelligence appeared first on Creative Commons.

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We need to secure immediate and long term funding for the CC licenses and CC0 public domain tool, which are key to building a healthy commons. We’re facing many challenges and threats to the commons–libraries are under attack, misinformation is rampant, and climate change threatens us all. CC is one of the few nonprofit, mission-driven organizations fighting to ensure we have a sound legal infrastructure backing open ecosystems, so that culture and knowledge are shared in order to foster understanding and find equitable solutions to our world’s most pressing challenges.

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