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

TL;DR

The paper investigates whether AI-enabled democratization of strategic knowledge undermines protection against manipulation in fair division algorithms. Using Spliddit and LLMs, it empirically demonstrates four manipulation scenarios where coordinated misreporting leads to sizable disparities while preserving envy-free guarantees, illustrated with a total rent of $36$ among five participants. Key contributions include showing that LLMs can explain algorithmic mechanics, identify profitable deviations, and produce concrete inputs for coordinated strategy, extending algorithmic collective action theory to resource allocation. The authors discuss societal implications, highlighting both risks to system integrity and potential for empowering historically disadvantaged groups when paired with participatory, fairness-focused design.

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.