AI brain composed of artworks distributing royalties.

Fair Play in AI: How to Solve Copyright Challenges and Reward Creativity

Elliot Brynn in Tech & Innovation June 2025 • 4 min read.

"Discover a groundbreaking economic solution that ensures fair compensation for artists and innovators in the age of generative AI."

Generative artificial intelligence (AI) is rapidly changing how we create and consume content. From generating realistic images to composing original music, AI's capabilities seem limitless. However, this technological revolution has sparked a heated debate about copyright, raising concerns about the rights of artists and creators whose work is used to train these powerful AI models.

Imagine a world where AI models are trained on vast datasets of copyrighted material without fairly compensating the original creators. This could stifle creativity, discourage artists from sharing their work, and ultimately hinder the progress of AI itself. The challenge lies in finding a balance—how can we foster innovation in AI while ensuring that artists and copyright owners receive fair recognition and reward for their contributions?

Enter a novel economic framework designed to address these very concerns. This approach leverages the probabilistic nature of modern AI models and principles from cooperative game theory to create a system where copyright owners are compensated proportionally to the value their data brings to AI-generated content. Let's explore how this system works and how it can pave the way for a more equitable and sustainable future for AI and the arts.

Unlocking Fair Compensation: The Shapley Royalty Share Framework

AI brain composed of artworks distributing royalties.

The proposed solution revolves around a two-step process called the Shapley Royalty Share (SRS) framework. This framework aims to quantify the contribution of each copyright owner's data to the AI's ability to generate new content. By determining the utility of each data source, the framework establishes a basis for fair distribution of royalties.

The first step involves evaluating the AI model's performance when trained on various subsets of the entire dataset. Think of it like this: the framework assesses how well the AI can generate content similar to the final output when trained on a specific artist's work, or a specific subset of data. This evaluation results in a utility score for each subset—the higher the score, the more valuable that data is considered to be.

Utility Assessment: Measures the impact of each data subset on AI's ability to generate target content.
Contribution Calculation: Quantifies the contribution of individual copyright owners using cooperative game theory.
Fair Distribution: Ensures royalties are distributed based on quantifiable contributions.

The second step employs tools from cooperative game theory, specifically the Shapley value, to determine each copyright owner's rightful share. The Shapley value is a concept that fairly distributes gains (or costs) among members of a coalition based on their individual contributions. In this context, it calculates how much each copyright owner's data contributes to the overall utility of the AI model. Owners whose data consistently increase the utility score across different combinations receive a larger share of the royalties.

A Win-Win for AI and the Arts

The Shapley Royalty Share framework offers a promising path toward resolving the copyright challenges posed by generative AI. By establishing a system that fairly compensates copyright owners, the framework fosters a more collaborative and sustainable ecosystem for AI development and creative expression. This approach encourages artists to contribute their work to AI training, knowing they will be recognized and rewarded for their valuable contributions. Ultimately, this will lead to more innovative and diverse AI models that benefit society as a whole.

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Everything You Need To Know

What is the central challenge regarding copyright and generative AI?

The core challenge lies in balancing AI innovation with the rights of artists and creators whose copyrighted work is used to train AI models. Specifically, it's about ensuring fair compensation and recognition for these contributions to prevent stifling creativity and discouraging artists from sharing their work. Without a system of proper compensation, it hinders both artistic progress and the development of AI itself.

How does the Shapley Royalty Share framework propose to solve the copyright issues related to AI training?

The Shapley Royalty Share (SRS) framework addresses copyright concerns by quantifying the contribution of each copyright owner's data to the AI's ability to generate new content. It uses a two-step process: first, it evaluates the AI model's performance when trained on subsets of the dataset to determine a utility score for each subset. Second, it employs the Shapley value from cooperative game theory to fairly distribute royalties based on each owner's contribution to the AI model's overall utility. Copyright owners receive royalties proportional to the value their data brings to AI-generated content.

Can you explain the two key steps in the Shapley Royalty Share framework?

Certainly. The first step is 'Utility Assessment,' which measures the impact of each data subset on the AI's ability to generate target content. The higher the utility score, the more valuable the data is considered. The second step is 'Contribution Calculation,' where cooperative game theory, specifically the Shapley value, is used to quantify the contribution of individual copyright owners. This determines each copyright owner's rightful share of royalties, ensuring fair distribution based on quantifiable contributions.

What is the Shapley value, and how is it applied within the Shapley Royalty Share framework?

The Shapley value is a concept from cooperative game theory that fairly distributes gains (or costs) among members of a coalition based on their individual contributions. In the Shapley Royalty Share (SRS) framework, the Shapley value calculates how much each copyright owner's data contributes to the overall utility of the AI model. It ensures that owners whose data consistently increase the utility score across different combinations receive a larger share of the royalties. This application promotes fairness and incentivizes valuable contributions to AI training datasets.

What are the potential benefits of implementing the Shapley Royalty Share framework for the AI and arts communities?

The Shapley Royalty Share (SRS) framework offers several benefits. It fosters a more collaborative and sustainable ecosystem for AI development and creative expression by fairly compensating copyright owners. This encourages artists to contribute their work to AI training, knowing they will be recognized and rewarded for their valuable contributions. This approach can lead to more innovative and diverse AI models, benefiting society as a whole by promoting a win-win scenario where AI advancements and artistic creativity are mutually supported.