Incentive Mechanism Design Toward a Win–Win Situation for Generative Art Trainers and Artists

Abstract

The recent development of generative art, a typical category of artificial intelligence-generated content (AIGC), is essentially beneficial for social good, which can help amateurs to create artwork and improve experts’ efficiency. However, some artists are opposed to generative art technologies due to the copyright infringement and influence of the artists’ way of earning a living, which makes the artists protest against generative art technologies, causing a lose–lose situation. Adversarial attacks against generative model training are potential solutions to address this issue, while the lose–lose situation cannot be improved. To build a win–win situation, a feasible method is to incentivize the artists to actively contribute their artworks to generative model training without influencing their living or infringing copyright, such as data crowdsourcing, but traditional data crowdsourcing methods cannot well fit the generative art area. Therefore, this article builds a blockchain-based trading system for generative model training data collection and generated artwork circulation. Specifically, this article formulates a social welfare maximization problem based on the reverse auction and designs a corresponding incentive mechanism. The conducted theoretical analysis and numerical evaluation demonstrate the effectiveness of the proposed incentive mechanism toward a win–win situation for generative art model trainers and artists.