Fairness Auditing with Multi-Agent Collaboration
Martijn de Vos Akash Dhasade Jade Garcia Bourrée Anne-Marie Kermarrec Erwan Le Merrer Benoit Rottembourg Gilles Tredan
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.