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An Economic Solution to Copyright Challenges of Generative AI

Author

Listed:
  • Jiachen T. Wang
  • Zhun Deng
  • Hiroaki Chiba-Okabe
  • Boaz Barak
  • Weijie J. Su

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.

Suggested Citation

  • Jiachen T. Wang & Zhun Deng & Hiroaki Chiba-Okabe & Boaz Barak & Weijie J. Su, 2024. "An Economic Solution to Copyright Challenges of Generative AI," Papers 2404.13964, arXiv.org, revised Sep 2024.
  • Handle: RePEc:arx:papers:2404.13964
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    References listed on IDEAS

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    1. Gilles, Robert P & Owen, Guillermo & van den Brink, Rene, 1992. "Games with Permission Structures: The Conjunctive Approach," International Journal of Game Theory, Springer;Game Theory Society, vol. 20(3), pages 277-293.
    2. Lei, Xiaochang, 2023. "Pro-rata vs User-centric in the music streaming industry," Economics Letters, Elsevier, vol. 226(C).
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    Cited by:

    1. Christian Peukert & Florian Abeillon & Jérémie Haese & Franziska Kaiser & Alexander Staub, 2024. "Strategic Behavior and AI Training Data," CESifo Working Paper Series 11099, CESifo.
    2. Christian Peukert & Florian Abeillon & J'er'emie Haese & Franziska Kaiser & Alexander Staub, 2024. "Strategic Behavior and AI Training Data," Papers 2404.18445, arXiv.org.

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