Content Platform GenAI Regulation via Compensation

A new research paper by Wee Chaimanowong, published on arXiv, tackles the growing problem of 'data pollution' caused by generative AI (GenAI). The paper, titled 'Content Platform GenAI Regulation via Compensation,' argues that the widespread, unregulated use of AI for content creation is creating a vicious cycle: AI models trained on human data now flood platforms with synthetic content, which then contaminates the data pool used to train future models. This distortion lowers the overall quality and engagement on platforms, ultimately hurting their profits. The research posits that this market failure stems from creators not being compensated for their work used in AI training, removing the incentive to produce high-value human content.

Chaimanowong's proposed solution is an economically-driven compensation scheme for creators. Instead of relying on imperfect and often circumvented AI-detection tools, the model suggests platforms can directly incentivize the creation of human-generated content by paying creators. This simple market intervention aims to rebalance the content ecosystem. By making human creation more economically viable, the scheme would naturally increase the proportion of high-quality human data, reduce synthetic data pollution for future AI training, and improve consumer engagement—all of which benefits the platform's bottom line. The 40-page paper presents this as a pragmatic, self-regulating alternative to top-down technical detection, framing it as a win-win for creators, AI developers, and platforms.