Summary of An Economic Solution to Copyright Challenges Of Generative Ai, by Jiachen T. Wang et al.
An Economic Solution to Copyright Challenges of Generative AI
by Jiachen T. Wang, Zhun Deng, Hiroaki Chiba-Okabe, Boaz Barak, Weijie J. Su
First submitted to arxiv on: 22 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: General Economics (econ.GN); Methodology (stat.ME)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a framework to address the growing concern that generative AI systems may infringe on the copyright interests of training data contributors. The framework compensates copyright owners proportionally to their contributions by leveraging the probabilistic nature of modern generative AI models and cooperative game theory techniques. This approach enables a platform where AI developers access high-quality training data, improving model performance, while copyright owners receive fair compensation, driving continued provision of relevant data for generative model training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The framework works by identifying the most relevant data sources used in artwork generation, ensuring a fair and interpretable distribution of revenues among copyright owners. The authors demonstrate the effectiveness of their approach through experiments that show the framework successfully recognizing the relevant data sources used to generate AI-produced artworks. |
Keywords
» Artificial intelligence » Generative model
