Statistical Collusion by Collectives on Learning Platforms

2025-02-07

AI Summary

This paper investigates how colluding users can collectively manipulate training data—such as product reviews—to influence decisions of learning-based platforms, focusing on the feasibility, risk constraints, and implementability of such statistical collusion. To address the challenges of pre-deployment impact assessment and the lack of theoretically grounded coordination mechanisms, the authors first systematically characterize the feasibility boundary of statistical collusion. They propose a decentralized coordination algorithmic framework grounded in observable data, integrating statistical inference, game-theoretic modeling, and counterfactual impact evaluation. The approach is empirically validated on real-world review datasets, demonstrating both effectiveness and robustness, while quantifying platform models’ sensitivity thresholds to coordinated perturbations. Key contributions include: (i) the first formal statistical collusion theory framework; (ii) risk-aware, pre-deployment impact assessment; and (iii) a deployable, distributed coordination mechanism for colluding agents.

Technology Category

• Game Theory and Economic Paradigms: Coordination and Collaboration
• Multiagent Systems: Coordination and Collaboration
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems

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.

Problem

• Detecting collective influence on platforms
• Developing coordination algorithms for collectives
• Assessing risks in data modification

Innovation

• Develops theoretical framework
• Implements coordination algorithms
• Assesses collective impact