Generative artificial intelligence (AI) systems are trained on large data
corpora to generate new pieces of text, images, videos, and other media. There
is growing concern that such systems may infringe on the copyright interests of
training data contributors. To address the copyright challenges of generative
AI, we propose a framework that compensates copyright owners proportionally to
their contributions to the creation of AI-generated content. The metric for
contributions is quantitatively determined by leveraging the probabilistic
nature of modern generative AI models and using techniques from cooperative
game theory in economics. This framework enables a platform where AI developers
benefit from access to high-quality training data, thus improving model
performance. Meanwhile, copyright owners receive fair compensation, driving the
continued provision of relevant data for generative model training. Experiments
demonstrate that our framework successfully identifies the most relevant data
sources used in artwork generation, ensuring a fair and interpretable
distribution of revenues among copyright owners.