Dear Open Movement Creators, Activists, and Stewards, 

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.