Copyright Throughout a Creative AI Pipeline
By Sancho McCann. 2021
6 citations recorded by the source corpus.
Consider the following fact pattern: Alex paints some original works on canvas and posts photos of them online. Becca downloads those images and uses them to train an AI (training configures the AI’s model parameters to useful values). Becca posts the resulting trained parameter values on her website under a license that reserves to Becca the right to use the parameters commercially. Cory uses those parameter values in a program that is designed to produce artwork. Cory clicks create and the program produces a work. This work is new to Cory, but it looks a lot like one of Alex’s original canvas images. Cory sells the work. Advise Cory about their potential copyright liability to Alex (for the substantially similar work that the program produced and that Cory subsequently sold) and to Becca (for taking Becca’s parameters and using them commercially, contrary to the license). Cory clicks create again. The program produces another work, this time quite different from any of Alex’s original paintings. Cory shares new work on Instagram. Danny copies this image from Cory’s Instagram feed and sells a bunch of postcards that feature that image. Advise Danny about their copyright liability to Cory. These scenarios are not as contrived as they might initially seem. Copyrighted works are frequently required to train an AI (more precisely: to train an AI’s parameters). The resulting trained parameters are being shared under licenses that assume that copyright applies. People do use these parameters in programs that can produce novel content. The resulting work can be quite surprising to the end-user and there are generally no checks in place to ensure that the new works don’t take too directly from the original training data. However, many of the new works will be quite different from any content already in the world. And the end-users of the creative program often claim copyright ownership over the resulting novel work. I will first present the training and use of a creative program based on a neural network, a popular model that forms the basis of state-of-the-art creative AIs. Then, I will examine each of the issues just raised: 1. Does the person managing the automatic training of a neural network’s parameters obtain a copyright in the resulting trained parameters? 2. Does a person using a program that produces artistic output obtain a copyright in that output? 3. The automatic training of a neural network requires large amounts of example data (a training set). Can images from around the internet be copied for the purpose of training a neural network? 4. What if a person uses an AI to produce a work that looks substantially similar to one of the training examples? Is that an infringement? And who is infringing?
Subfields: AI Ethics, Fairness & Society
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Citations in the corpus
- Foundation Models and Fair Use (2023, background): “McCann (2021) suggests that Canadian law might follow a similar approach to what we describe here, where generative models might have to follow Canada’s fair dealing doctrine.”