Artificial Intelligence's Fair Use Crisis by Benjamin Sobel :: SSRN

As automation supplants more forms of labor, creative expression still seems like a distinctly human enterprise. This may someday change: by ingesting works of authorship as "training data," computer programs can teach themselves to write natural prose, compose music, and generate movies. However, current fair use doctrine threatens either to derail the progress of machine learning or to disenfranchise the human creators whose work makes it possible. It concludes that fair use may not protect expressive machine learning applications, including the burgeoning field of natural language generation. Part II explains that applying today's fair use doctrine to expressive machine learning will yield one of two undesirable outcomes: if US courts reject the fair use defense for machine learning, valuable innovation may move to another jurisdiction or halt entirely; alternatively, if courts find the technology to be fair use, sophisticated software may divert rightful earnings from the authors of input data.