Generative AI companies are being sued over their use of data (specifically images and code) scraped from the web to train their models. Once trained, such models can generate, on demand, images in a given artist’s style or code that executes particular tasks.
The lawsuits will answer the question of whether using publicly available data to train generative models is legal.
To be clear, this issue is much bigger than generative AI. The fundamental question is whether AI systems should be allowed to learn from data that’s freely available to anyone with an internet connection. But the focus right now is on models that generate images and code.
Getty Museum in Los Angeles, California, is seen flooded with aspiring artists sitting on the floor and copying masterpieces on their own canvases. Copying the masters is an accepted part of learning to be an artist. By copying many paintings, students develop their own style. Artists also routinely look at other works for inspiration. Even the masters whose works are studied today learned from their predecessors. Is it fair for an AI system, similarly, to learn from paintings created by humans?
Of course, there are important differences between human learning and machine learning that bear on fairness. A machine learning model can read far more code and study far more images than a human can. It can also generate far more code or images, far more quickly and cheaply, than even the most skilled human.
The upshot is that we need to make difficult tradeoffs between enabling technological progress and respecting the desire to protect creators’ livelihoods. Thoughtful regulation can play an important role. One can imagine potential regulatory frameworks such as:
- Establishing a consistent way for creators to opt out
- Mandating compensation for artists when AI systems use their data
- Allocating public funding to artists (like using tax dollars to fund public media such as the BBC)
- Setting a time limit, like copyright, after which creative works are available for AI training