An Economic Solution to Copyright Challenges of Generative AI

TL;DR

This paper proposes an economic framework that fairly compensates copyright owners based on their contribution to AI-generated content, using probabilistic models and game theory to ensure fair revenue distribution.

Contribution

It introduces a novel, quantitative contribution metric for copyright owners in generative AI, combining probabilistic modeling and cooperative game theory for fair compensation.

Findings

Framework accurately identifies relevant data sources in artwork generation.

Ensures fair and interpretable revenue distribution among copyright owners.

Enhances data sharing incentives for AI development.

Abstract

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.