An Economic Solution to Copyright Challenges of Generative AI

An Economic Solution to Copyright Challenges of Generative AI

April 24, 2024 | Jiachen T. Wang, Zhun Deng, Hiroaki Chiba-Okabe, Boaz Barak, Weijie J. Su
The paper "An Economic Solution to Copyright Challenges of Generative AI" addresses the growing concern of copyright infringement by generative AI systems trained on large datasets. The authors propose a framework that compensates copyright owners proportionally to their contributions to AI-generated content. This framework leverages the probabilistic nature of modern generative AI models and cooperative game theory to determine the utility of different data sources and distribute royalties fairly among copyright owners. The Shapley value, a solution concept in cooperative game theory, is used to measure the utility of each data source and ensure fair and interpretable revenue distribution. Experiments using the WikiArt and FlickrLogo-27 datasets demonstrate the effectiveness of the framework in identifying relevant copyright owners and attributing contributions accurately. The paper also discusses limitations and future directions, including the need to address strategic behaviors and handle scenarios with multiple small datasets.The paper "An Economic Solution to Copyright Challenges of Generative AI" addresses the growing concern of copyright infringement by generative AI systems trained on large datasets. The authors propose a framework that compensates copyright owners proportionally to their contributions to AI-generated content. This framework leverages the probabilistic nature of modern generative AI models and cooperative game theory to determine the utility of different data sources and distribute royalties fairly among copyright owners. The Shapley value, a solution concept in cooperative game theory, is used to measure the utility of each data source and ensure fair and interpretable revenue distribution. Experiments using the WikiArt and FlickrLogo-27 datasets demonstrate the effectiveness of the framework in identifying relevant copyright owners and attributing contributions accurately. The paper also discusses limitations and future directions, including the need to address strategic behaviors and handle scenarios with multiple small datasets.
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