Groundbreaking Court Rulings Could Upend the Future of AI Training and Copyright—What You Need to Know Now
The second is the training work that is involved in building an LLM, that is the encoding of the knowledge of the books ingested. This was deemed to be transformative in both cases and can be reasonably understood in these terms. If this were the extent of the things that LLMs were doing with content, one might consider these judgements as simple wins.
The third touchpoint to copyright is the output of the LLM systems. An LLM system could be used to create a completely new work in the style of a previous work, which is widely recognized as transformative. However, the same system could easily be used to produce a passingly similar copy, or a follow-on book where the first book left off, fanfiction, or an image that is as closely representative as to be passable to an average consumer. It seems that in both cases, the judges were too enamored in the transformative nature of the output that is possible that they ignored the possibility of near exact replication. Here there are a variety of examples and interpretations of transformative use related to the output itself. Since LLMs are not simply indexing the content, or creating a search functionality, but generating new content based on the original content, one must consider the actual outputs in a fair use determination. Some of the outputs might be new and novel, fitting into established fair use buckets of transformation. However, based on the rich case law, just as many likely are not. When considering the fair use tests, it is therefore surprising that the cases so far have only revolved around the training, but not the outputs, since these are the real purpose of the systems, not simply the training of the model. One response is that the judges were reacting to the cases put before them. I expect this final issue will come to the forefront in other cases.
