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Making AI work for Creators and the Commons

“CC Global Summit 2023 Day 0” by Creative Commons is licensed CC BY 4.0.

On the eve of the CC Global Summit, members of the CC global community and Creative Commons held a one-day workshop to discuss issues related to AI, creators, and the commons. The community attending the Summit has a long history of hosting these intimate discussions before the Summit begins on critical and timely issues.

Emerging from that deep discussion and in subsequent conversation during the three days of the Summit, this group identified a set of common issues and values, which are captured in the statement below. These ideas are shared here for further community discussion and to help CC and the global community navigate uncharted waters in the face of generative AI and its impact on the commons.

Background considerations

Recognizing that around the globe the legal status of using copyright protected works for training generative AI systems raises many questions and that there is currently only a limited number of jurisdictions with relatively clear and actionable legal frameworks for such uses.* We see the need for establishing a number of principles that address the position of creators, the people building and using machine learning (ML) systems, and the commons, under this emerging technological paradigm.
Noting that there are calls from organized rightholders to address the issues posed by the use of copyrighted works for training generative AI models, including based on the principles of credit, consent, and compensation.
Noting that the development and deployment of generative AI models can be capital intensive, and thus risks resembling (or exacerbating) the concentration of markets, technology, and power in the hands of a small number of powerful for-profit entities largely concentrated in the United States and China, and that currently most of the (speculative) value accrues to these companies.
Further noting that, while the ability for everyone to build on the global information commons has many benefits, the extraction of value from the commons may also reinforce existing power imbalances and in fact can structurally resemble prior examples of colonialist accumulation.

Noting that this issue is especially urgent when it comes to the use of traditional knowledge materials as training data for AI models.
Noting that the development of generative AI reproduces patterns of the colonial era, with the countries of the Global South being consumers of Northern algorithms and data providers.

Recognizing that some societal impacts and risks resulting from the emergence of generative AI technologies need to be addressed through public regulation other than copyright, or through other means, such as the development of technical standards and norms. Private rightsholder concerns are just one of a number of societal concerns that have arisen in response to the emergence of AI.
Noting that the development of generative AI models offers new opportunities for creators, researchers, educators, and other practitioners working in the public interest, as well as providing benefits to a wide range of activities across other sectors of society. Further noting that generative AI models are a tool that enables new ways of creation, and that history has shown that new technological capacities will inevitably be incorporated into artistic creation and information production.


We have formulated the following seven principles for regulating generative AI models in order to protect the interests of creators, people building on the commons (including through AI), and society’s interests in the sustainability of the commons:

It is important that people continue to have the ability to study and analyse existing works in order to create new ones. The law should continue to leave room for people to do so, including through the use of machines, while addressing societal concerns arising from the emergence of generative AI.
All parties should work together to define ways for creators and rightsholders to express their preferences regarding AI training for their copyrighted works. In the context of an enforceable right, the ability to opt out from such uses must be considered the legislative ceiling, as opt-in and consent-based approaches would lock away large swaths of the commons due to the excessive length and scope of copyright protection, as well as the fact that most works are not actively managed in any way.
In addition, all parties must also work together to address implications for other rights and interests (e.g. data protection, use of a person’s likeness or identity). This would likely involve interventions through frameworks other than copyright.
Special attention must be paid to the use of traditional knowledge materials for training AI systems including ways for community stewards to provide or revoke authorisation.
Any legal regime must ensure that the use of copyright protected works for training generative AI systems for noncommercial public interest purposes, including scientific research and education, are allowed.
Ensure that generative AI results in broadly shared economic prosperity – the benefits derived by developers of AI models from access to the commons and copyrighted works should be broadly shared among all contributors to the commons.
To counterbalance the current concentration of resources in the the hands of a small number of companies these measures need to be flanked by public investment into public computational infrastructures that serve the needs of public interest users of this technology on a global scale. In addition there also needs to be public investment into training data sets that respect the principles outlined above and are stewarded as commons.

* For a survey of legal frameworks around the work, see the Global Congress on Intellectual Property and the Public Interest.

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