An Economic Solution to Copyright Challenges of Generative AI
(2404.13964)Abstract
Generative 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.
Overview
-
The paper introduces a novel economic framework using cooperative game theory to address copyright challenges in generative AI without compromising AI capabilities. It proposes a revenue-sharing system which fairly compensates copyright owners based on their data's impact on the generated content.
-
In the proposed model, the Shapley value from game theory is used to quantitatively assign revenue shares by assessing each data source's incremental utility. This allows for fair and precise compensation according to the relative importance of each contributor's data in the generated content.
-
Experimental tests on datasets like WikiArt and FlickrLogo-27 show the model's effectiveness in equitable revenue distribution among stakeholders for both style-specific and mixed-style content generation. The paper also discusses potential future improvements and broader applications of the model.
Tackling Copyright Challenges in Generative AI through Economic Models
Introduction
Generative AI has transformed the creative industries by producing high-level content from vast data pools of existing human-created material. However, this capability comes with significant legal and ethical concerns, notably copyright infringement. Existing solutions often involve limiting data usage or modifying generative processes, which can degrade model performance. This paper proposes a novel economic framework that establishes a revenue-sharing system between AI developers and copyright owners without compromising the generative capabilities of AI models.
Economic Model Proposal
The core of the proposed solution involves a framework that utilizes cooperative game theory to equitably distribute revenue among copyright stakeholders. This system operates by quantitatively assessing each data source's contribution to the generated content using a metric derived from the probabilistic outputs of generative models. Specifically, contributions are calculated using the Shapley value—a concept from game theory which ensures a fair distribution of payoffs based on individual contributions to the collective output.
Framework Application
- Utility Calculation: The utility of data subsets is measured by their likelihood to produce the desired output compared to a model trained on the entire dataset.
- Shapley Value Calculation: Each copyright owner's share is quantitatively determined based on the incremental utility their data brings when combined with various subsets of other data.
Experimental Results
The implementation of the framework was tested on two datasets — WikiArt and FlickrLogo-27 — focusing on the generation of artistic images and logos. These experiments demonstrated the framework's capability to:
- Attribute revenue shares commensurate with the data's relevance to the generated content.
- Recognize and reward the contributions of diverse data sources in mixed-style content generation.
- Distribute revenue fairly among stakeholders when generating content unrelated to specific copyrighted sources.
Highlights from Tests
- Style-Specific Generation: For AI-generated artworks inspired by specific artists, the framework successfully aligns higher Shapley values with artists whose styles were emulated.
- Mixed-Style Generation: When tasked to blend styles from multiple artists, the framework accurately measured and compensated based on each style's influence on the final artwork.
- Generalization Capability: The framework proved robust across various scenarios, including generating content without direct data antecedents.
Theoretical Implications and Future Directions
This paper not only contributes a practical solution to copyright disputes in the realm of AI but also opens up new avenues for applying cooperative game theory in complex AI challenges. The adoption of the Shapley value provides a robust basis for equitable economic transactions in AI-related activities and could influence broader applications in other sectors where data sharing is crucial.
Looking forward, expanding this framework could involve:
- Scalability Improvements: Enhancing computational methods for Shapley value calculation to handle larger data sets and more complex model architectures more efficiently.
- Applicability Extension: Beyond copyright issues, this model could be adapted for revenue sharing among data collaborators in various AI-driven consortia or joint ventures.
Concluding Remarks
The proposed economic framework offers a promising solution to the contentious issue of copyright in generative AI, equipping stakeholders with a tool that respects legal boundaries while fostering technological advancement. With further refinement and adaptation, such approaches have the potential to mitigate significant barriers to innovation in AI and other data-driven domains.
Create an account to read this summary for free: