Statistical Collusion by Collectives on Learning Platforms

TL;DR

This paper develops a theoretical and algorithmic framework to understand how collectives can coordinate data manipulation to influence learning platforms, highlighting the risks and strategies involved.

Contribution

It introduces a novel framework for analyzing collective data manipulation, including assessments and coordination algorithms, with experimental validation in product evaluation.

Findings

Collectives can strategically influence learning platforms through coordinated data submission.

The framework enables assessment of impact and development of implementable coordination algorithms.

Experimental results demonstrate the effectiveness of the proposed methods.

Abstract

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.