Market Dynamics and Regulation of a Crowd-Sourced AI Marketplace

Abstract

As usage of artificial intelligence (AI) technologies across industries increases, there is a growing need for creating large marketplaces to host and transact good-quality data sets to train AI algorithms. Our study analyzes the characteristics of such an oligopsony crowdsourced AI Marketplace (AIM) that has a large number of producers and few consumers who transact data sets as per their expectations of price and quality. Using agent-based modeling (ABM), we incorporate heterogeneity in agent attributes and self-learning by the agents that are reflective of real-world marketplaces. Our research augments the existing studies on the effect of and reputation systems in such market places. Extensive simulations using ABM indicate that ratings of the data sets as a feedback mechanism plays an important role in improving the quality of said data sets, and hence the reputations of producers. While such marketplaces are evolving, regulators have started enacting varying rules to oversee the appropriate functioning of such marketplaces, to minimize market distortions. In one of the first such studies, we integrate regulatory interventions in a marketplace model to analyze the impacts of various types of regulations on the functioning of an AIM. Our results indicate that very stringent regulatory measures negatively affect the production of quality data sets in the marketplace. On the other hand, regulatory oversight along with a ratings-based feedback mechanism improves the functioning of an AIM, and hence is recommended for governments and policy makers to adopt.

Keywords

• Crowd-sourced
• oligopsony
• quality sensitivity
• regulation
• reputation systems
• trust