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An Economic Solution to Copyright Challenges of Generative AI
April 23, 2024, 4:42 a.m. | iachen T. Wang, Zhun Deng, Hiroaki Chiba-Okabe, Boaz Barak, Weijie J. Su
cs.LG updates on arXiv.org arxiv.org
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 …
abstract artificial artificial intelligence arxiv challenges contributors copyright cs.lg data econ.gn economic generate generative generative artificial intelligence images intelligence media q-fin.ec solution stat.me systems text training training data type videos
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