Content Platform GenAI Regulation via Compensation

TL;DR

This paper proposes an economically-driven creator compensation scheme for content platforms using GenAI, aiming to incentivize high-value human content, reduce data pollution, and improve platform engagement and profit.

Contribution

It introduces a simple compensation mechanism that balances creator incentives and platform interests, addressing GenAI training data issues and content quality.

Findings

Compensation scheme incentivizes more high-value human content.

Reduces data pollution in GenAI training datasets.

Enhances consumer engagement and platform profit.

Abstract

The use of Generative AI (GenAI) for creative content generation has gained popularity in recent years. GenAI allows creators to generate contents that are increasingly becoming indistinguishable to the human--generated counter--part at a much lower cost. While GenAI reshapes the competitive landscape of the contents market, the original creators were typically not compensated for their works that were used in the GenAI training. On the other hands, the wide--spread adoption of GenAI threatens to replace the human--generated shares of contents on content platforms, contaminating training data source for future GenAI models. In this paper, we argue that an unregulated usage of GenAI can also be harmful to the platform by causing a contents distribution distortion which can lower the consumers' engagement and the platform's profit. We show that a simple economically--driven creator compensation scheme, can incentivize more creation of high--value human--generated contents, without the need for an AI--detector. This reduces the data pollution for future GenAI training, while improves the consumer engagement and the platform's profit.