Balanced scale symbolizing the balance between artistic creation and AI technology.

Fair Play in the AI Era: How to Compensate Copyright Owners and Fuel Innovation

Avery Sinclair in Tech & Innovation August 2025 • 5 min read.

"Explore a groundbreaking economic model that balances the rights of copyright holders with the relentless advancement of generative AI, ensuring a sustainable future for creative content."

The rise of generative artificial intelligence (AI) has sparked a creative revolution, enabling the creation of text, images, videos, and music with unprecedented ease. These AI systems, fueled by vast datasets of human-created content, are rapidly reshaping industries and challenging traditional notions of authorship. However, this technological leap has also ignited a fierce debate: how do we ensure that the copyright holders of training data are fairly compensated for their contributions to these powerful AI models?

The concern is legitimate. Generative AI models learn from massive datasets, often including copyrighted material, to produce new content. This raises the specter of copyright infringement and has led to lawsuits against AI companies. Current efforts to mitigate these issues often involve modifying AI training or inference processes to avoid generating infringing outputs. But these modifications can compromise model performance by excluding high-quality, copyrighted data or restricting content generation.

Instead of restricting the use of copyrighted data, a more sustainable solution lies in establishing a mutually beneficial revenue-sharing agreement between AI developers and copyright owners. This article introduces a simple yet powerful framework that appropriately compensates copyright owners for the use of their data in training generative AI systems, using principles from cooperative game theory to navigate the complexities of copyright challenges. The goal? To foster innovation while guaranteeing a fair share of the benefits to all copyright holders.

The Shapley Royalty Share: A Fair Framework for AI and Copyright

Balanced scale symbolizing the balance between artistic creation and AI technology.

The proposed framework tackles copyright issues with a two-pronged approach, starting with utility evaluation. This involves assessing the value of a generative AI model trained on every possible subset of the entire dataset. The utility of a specific data subset is considered high if the resulting model can generate AI content similar to that produced by a model trained on the complete dataset. In simpler terms, it measures how much each piece of the data helps the AI to achieve the desired result.

Once the utility of each data subset is determined, the next step is to determine the rightful share for each participating copyright owner. This is where the Shapley value, a concept from cooperative game theory, comes into play. The Shapley value provides a way to distribute gains (or costs) based on the contribution of each player in a coalition. In this context, a copyright owner's share is higher if their data tends to increase the overall utility of the model.

Utilities of Different Data Source Combinations: Imagine 'n' copyright owners, each holding the rights to training data D(i). The deployed model, trained on the entire dataset D, generates content x(gen). To assess the utility, consider a counterfactual model trained on a subset of data, and evaluate its likelihood of generating the same content x(gen).
The Utility Metric: The utility is measured using the log-likelihood of generating the user-chosen content. This metric reflects the model's capability to satisfy user needs. Royalties are distributed proportionally to each copyright owner's contribution, determined analytically using the Shapley value.
Ensuring Interpretability: By aligning compensation with quantifiable contributions, the framework ensures transparency and fosters innovation in AI, guaranteeing a fair share of benefits for all copyright holders.

The beauty of this framework is that it doesn't require modifying the AI model's inference process, preserving its full capabilities. By leveraging the probabilistic nature of generative models, the framework uses the log-likelihood of generating user-chosen content to measure the utility of the training data. This utility measure captures the model's ability to satisfy user needs. Royalties are then distributed among copyright owners according to their contributions, determined using the Shapley value. This ensures that compensation aligns with quantifiable contributions, fostering innovation in AI while guaranteeing a fair share of benefits to all copyright holders.

Toward a Sustainable AI Ecosystem

The proposed royalty-sharing model represents a significant step towards resolving the complex copyright challenges posed by generative AI. By fairly compensating copyright owners for their contributions, this framework fosters a sustainable ecosystem where AI developers can access high-quality training data, and content creators are incentivized to continue providing valuable input. This approach not only addresses legal concerns but also promotes ethical practices and encourages continued innovation in the field of artificial intelligence.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2404.13964,

Title: An Economic Solution To Copyright Challenges Of Generative Ai

Subject: cs.lg econ.gn q-fin.ec stat.me

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

Published: 22-04-2024

Everything You Need To Know

What is the core problem that this framework aims to solve in the context of generative AI?

The core problem addressed by the framework is how to fairly compensate copyright owners for their content used in training generative AI models. Generative AI models use vast datasets, often including copyrighted material, which raises concerns about copyright infringement. The framework seeks to ensure that copyright holders are justly rewarded for their contributions, promoting a sustainable ecosystem where AI developers can access data and creators are incentivized to contribute valuable content. This approach moves away from restricting data use and instead focuses on a revenue-sharing agreement between AI developers and copyright owners.

How does the 'Shapley Royalty Share' framework determine the value of a copyright owner's data?

The 'Shapley Royalty Share' framework uses a two-pronged approach: utility evaluation and the Shapley value. Utility evaluation assesses the value of a generative AI model trained on different subsets of the data. The utility of a data subset is considered high if the resulting model can generate AI content similar to that produced by a model trained on the complete dataset. The Shapley value, from cooperative game theory, is then used to distribute gains based on the contribution of each copyright owner's data. A copyright owner's share is higher if their data significantly increases the model's overall utility. The utility is measured using the log-likelihood of generating user-chosen content.

What is the role of utility evaluation in the proposed framework and how is it measured?

Utility evaluation is a crucial step in the framework to determine the value of different data subsets used to train the generative AI model. It helps to understand how much each piece of data contributes to the overall performance of the model. The utility is measured by considering a counterfactual model trained on a subset of data and evaluating its likelihood of generating the same content as the model trained on the complete dataset. More specifically, the utility is measured using the log-likelihood of generating the user-chosen content. This metric reflects the model's capability to satisfy user needs, quantifying how well the model performs with a particular data subset.

How does the proposed framework ensure fairness and transparency in compensating copyright owners?

The framework ensures fairness and transparency by aligning compensation with quantifiable contributions, primarily through the use of the Shapley value. The Shapley value provides a method to distribute royalties based on the individual contribution of each copyright owner's data to the model's utility. This means that copyright owners whose data significantly improves the model's ability to generate desired content receive a larger share of the royalties. By quantifying the contribution of each data source and distributing royalties proportionally, the framework ensures that compensation is directly related to the value provided, fostering transparency and trust among all stakeholders.

Why is the 'Shapley Royalty Share' framework considered a more sustainable solution than modifying the AI model's inference process?

The 'Shapley Royalty Share' framework is considered more sustainable because it doesn't require modifying the AI model's inference process. Modifying the inference process to avoid copyrighted data can compromise model performance by excluding high-quality data or restricting content generation. The framework preserves the full capabilities of the AI model by using the probabilistic nature of generative models and leveraging the log-likelihood of generating user-chosen content to measure the utility of the training data. This allows the AI developers to use a wider range of data and helps to improve the AI model performance. By focusing on fair compensation through revenue sharing, it addresses the concerns of copyright owners without hindering the innovation or the capabilities of AI models.