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.

翻译：传统上，公平资源分配算法（如Spliddit平台所实现的算法）因其复杂性，通常被认为难以被终端用户操纵。本文展示了大型语言模型（LLM）如何通过民主化获取策略专业知识来瓦解这些保护屏障。通过对Spliddit算法中租金分配场景的实证分析，我们证明用户可以通过向AI助手提出简单的对话查询来获得可操作的操纵策略。我们提出了四种不同的操纵场景：多数群体剥削少数群体的排他性合谋、适得其反的防御性反制策略、对特定参与者的善意补贴以及成本最小化联盟。我们的实验表明，LLM能够解释算法机制、识别有利可图的偏差，并为协调性偏好误报生成具体的数值输入——这些能力以往需要深厚的技术知识。这些发现将算法集体行动理论从分类情境扩展到资源分配场景，其中协调性偏好操纵取代了特征操纵。其影响不仅限于租金分配，还延伸至任何使用算法公平机制进行资源分配的领域。虽然AI赋能的操纵对系统完整性构成风险，但也为应得公平待遇的群体创造了获得优待的机会。我们认为，有效的应对措施必须结合算法鲁棒性、参与式设计以及公平获取AI能力，并承认策略复杂性已不再是一种稀缺资源。