Why Is AI Art Copyright So Complicated?

Despite claims that machines and robots are now making art on their own, we are actually seeing the number of humans involved with creating a singular artwork go up, not down, with the introduction of machine learning-based tools.

Claims that AI is creating art on its own and that machines are somehow entitled to copyright for this art are simply naive or overblown, and they cloud real concerns about authorship disputes between humans. The introduction of machine learning as an art tool is ironically increasing human involvement, not decreasing it. Specifically, the number of people who can potentially be credited as coauthors of an artwork has skyrocketed. This is because machine learning tools are typically built on a stack of software solutions, each layer having been designed by individual persons or groups of people, all of whom are potential candidates for authorial credit.

This concept of group authorship that machine learning tools introduces is relatively incompatible with the traditional art market, which prefers singular authorship because that model streamlines sales and supports the concept of the individual artistic genius. Add to that the fact that AI art - and more broadly speaking, generative art - are algorithmic in nature (highly repeatable) and frequently open source (highly shareable), and you have a powder keg of potential authorial and copyright disputes.

The most broadly publicized case of this was the Edmond Belamy work that was sold by the French artist collective Obvious through Christie’s last summer for $432k. I have already explored that case ad nauseum (including an in-depth interview with the collective). I cite it here only to point out that there were a large number of humans that were involved in creating a work that was initially publicized as having been created by a machine.

In this article we look in detail at the recent GANbreeder incident (which we outline below) that has received some attention in the mainstream press. This is another case where the complexity of machine learning has driven up, not down, the number of humans involved with the creation of art and led to a great deal of misunderstanding and hurt feelings.

For this article I spoke with several people involved in the incident:

Danielle Baskin, the artist who alleges that Alexander Reben used her and other people’s images from GANbreeder

Alexander Reben, the artist accused of using other people’s GANbreeder images

Joel Simon, the creator of GANbreeder

I was also lucky enough to speak with Jessica Fjeld, an attorney with the Harvard Cyberlaw Clinic, who has written about and researched issues involving AI-generated art relative to copyright and licensing. She is the first lawyer I have spoken with who truly understands the nuances of law, machine learning, and artistic practice.