30 Creative Commons and Generative AI

Creative Commons has always sought out ways to harness new technology to serve the public interest and to support better sharing of creative content — sharing that is inclusive, just, equitable, reciprocal and sustainable. CC supports creators to share their works as broadly and openly as they want, so that people can enjoy them globally without unnecessary barriers. CC also advocates for policies that ensure new and existing creators are able to build on a shared commons, while respecting creators’ legitimate interests in control and compensation for their creative expressions.

A founding insight of Creative Commons is that all creativity build on the past. When people learn to play the cello or paint a picture, for instance, they necessarily learn from and train their own skills by engaging pre-existing works and artists — for instance, noticing the style in which cellists like Yo-Yo Ma arrange notes, or building on surrealist styles initiated by artists like Dali. Similarly, while Star Wars invented the character of Luke Skywalker, it built on the idea of the hero’s journey, among many other elements from past works. People observe the ideas, styles, genres, and other tropes of past creativity, and use what they learn to create anew. No creativity happens in a vacuum, purely original and separate from what’s come before.

Generative AI can function in a similar way. Just as people learn from past works, generative AI is trained on previous works, analyzing past materials in order to extract underlying ideas and other information in order to build new works. Image generation tools like Stable Diffusion develop representations of what images are supposed to look like by examining preexisting works associating terms like “dog” or “table” with shapes and colors such that a text prompt of those terms can then output images.

Given how digital technologies function, training AI in this way necessarily involves making an initial copy of images in order to analyze them. As we’ve explored in the past and will discuss in future posts about these recent lawsuits, we think this sort of copying can and should be permissible under copyright law. There are certainly nuances when it comes to copyright’s interaction with these tools — for instance, what if the tools are later used by someone to generate an output that does copy from a specific creative expression? But treating copying to train AI as per se infringing copyright would in effect shrink the commons and impede others’ creativity in an over-broad way. It would expand copyright to give certain creators a monopoly over ideas, genres, and other concepts not limited to a specific creative expression, as well as over new tools for creativity.

“Better Sharing for Generative AI.” Creative Commons, 6 Feb. 2023, https://creativecommons.org/2023/02/06/better-sharing-for-generative-ai/. CC BY 4.0.