Existing work in fairness audits assumes that agents operate independently. In this paper, we consider the case of multiple agents auditing the same platform for different tasks. Agents have two levers: their collaboration strategy, with or without coordination beforehand, and their sampling method. We theoretically study their interplay when agents operate independently or collaborate. We prove that, surprisingly, coordination can sometimes be detrimental to audit accuracy, whereas uncoordinated collaboration generally yields good results. Experimentation on real-world datasets confirms this observation, as the audit accuracy of uncoordinated collaboration matches that of collaborative optimal sampling.

翻译：现有公平性审计工作假设智能体独立运作。本文考虑多个智能体针对不同任务对同一平台进行审计的场景。智能体拥有两种可操控手段：协作策略（是否预先协调）与采样方法。我们从理论上研究了智能体独立运作或协作时这两种手段的交互作用。耐人寻味的是，我们证明协调有时反而会损害审计精度，而未经协调的协作通常能取得良好效果。在真实数据集上的实验验证了这一观察结果：未经协调的协作审计精度与协作最优采样方法相当。