When AI Democratizes Exploitation: LLM-Assisted Strategic Manipulation of Fair Division Algorithms

2025-11-18

AI Summary

Large language models (LLMs) significantly lower the technical barrier for users to strategically manipulate fair resource allocation algorithms—such as those deployed on Spliddit—thereby threatening algorithmic fairness and equity. Method: Building upon algorithmic collective action theory, we extend it to resource allocation and introduce the novel paradigm of *coordinated preference manipulation*. We identify four previously unrecognized manipulation scenarios—including exclusive collusion and cost-minimizing coalitions—and combine empirical analysis with interactive AI experiments: LLMs reverse-engineer algorithmic mechanisms, detect biases, and generate executable, coordinated false preference inputs. Contribution/Results: Our experiments demonstrate that LLMs efficiently produce actionable manipulation strategies. Such capabilities can undermine system fairness but also empower disadvantaged groups to achieve more equitable outcomes. This duality underscores AI’s dual role in fairness governance: as both a vulnerability vector and an instrument for redistributive justice. The work provides the first systematic framework for analyzing strategic manipulation in fair division via generative AI.

Technology Category

Game Theory and Economic Paradigms: Fair DivisionPhilosophy and Ethics of AI: Bias, Fairness & EquityHumans and AI: Learning Human Values and Preferences

Application Category

Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systemsUser Modeling, Personalization and Recommendation: Fairness-aware retrieval and rankingResponsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Fair resource division algorithms, like those implemented in Spliddit platform, have traditionally been considered difficult for the end users to manipulate due to its complexities. This paper demonstrates how Large Language Models (LLMs) can dismantle these protective barriers by democratizing access to strategic expertise. Through empirical analysis of rent division scenarios on Spliddit algorithms, we show that users can obtain actionable manipulation strategies via simple conversational queries to AI assistants. We present four distinct manipulation scenarios: exclusionary collusion where majorities exploit minorities, defensive counterstrategies that backfire, benevolent subsidization of specific participants, and cost minimization coalitions. Our experiments reveal that LLMs can explain algorithmic mechanics, identify profitable deviations, and generate specific numerical inputs for coordinated preference misreporting--capabilities previously requiring deep technical knowledge. These findings extend algorithmic collective action theory from classification contexts to resource allocation scenarios, where coordinated preference manipulation replaces feature manipulation. The implications reach beyond rent division to any domain using algorithmic fairness mechanisms for resource division. While AI-enabled manipulation poses risks to system integrity, it also creates opportunities for preferential treatment of equity deserving groups. We argue that effective responses must combine algorithmic robustness, participatory design, and equitable access to AI capabilities, acknowledging that strategic sophistication is no longer a scarce resource.

Problem

• LLMs enable strategic manipulation of fair division algorithms by users
• AI democratizes access to coordinated preference misreporting in resource allocation
• Algorithmic collective action theory extends from classification to resource division

Innovation

• LLMs generate strategic preference misreporting inputs
• AI assistants democratize access to manipulation expertise
• Coordinate algorithmic collective action for fair division