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.

一次训练中的数据 Shapley

Data Shapley in One Training Run

This article investigates how AI-generated content can disrupt central
revenue streams of the creative industries, in particular the collection of
dividends from intellectual property (IP) rights. It reviews the IP and
copyright questions related to the input and output of generative AI systems. A
systematic method is proposed to assess whether AI-generated outputs,
especially images, infringe previous copyrights, using a similarity metric
(CLIP) between images against historical copyright rulings. An examination
(economic and technical feasibility) of previously proposed compensation
frameworks reveals their financial implications for creatives and IP holders.
Lastly, we propose a novel IP framework for compensation of artists and IP
holders based on their published "licensed AIs" as a new medium and asset from
which to collect AI royalties.

探讨人工智能生成内容如何扰乱创意产业的核心收入来源，特别是知识产权（IP）权益的分红。通过审核人工智能生成系统的输入与输出之间的知识产权和版权问题，提出了一种系统性方法，使用图像相似度度量（CLIP）将图像与历史版权裁决进行比较，以评估 AI 生成的产出，尤其是图像是否侵犯了之前的版权。对先前提出的补偿框架进行经济和技术可行性审查，揭示了这些框架对创意者和知识产权持有人的财务影响。最后，提出了一个新颖的知识产权框架，以基于他们发布的 “许可 AI” 作为新媒介和资产来收取人工智能版税。