Statistical Collusion by Collectives on Learning Platforms
This research addresses the problem of statistical collusion for online learning platforms and their users, which is an incremental step in understanding the potential risks and mitigation strategies for such behavior.
This study tackles the problem of statistical collusion by collectives on learning platforms, where collectives can influence platforms by coordinated submission of altered data, and presents a framework to understand and address this issue. The study provides experimental results in a product evaluation domain.
As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular, collectives need to make a priori assessments of the effect of the collective before taking action, as they may face potential risks when modifying their data. Moreover they need to develop implementable coordination algorithms based on quantities that can be inferred from observed data. We develop a framework that provides a theoretical and algorithmic treatment of these issues and present experimental results in a product evaluation domain.