For instance, (Rastegarpanah Existing work in fairness audits assumes that et al., 2021) proposes to study the illegal use of some profile agents operate independently. In this paper, we data in the response of a model in such a query-response consider the case of multiple agents auditing the setup. Yet, as of today, an auditor performs her audit tasks same platform for different tasks. Agents have on each attribute of interest sequentially, one after the other, two levers: their collaboration strategy, with or and independent of other auditors. For example, when she without coordination beforehand, and their sampling wants to audit an ML model that predicts whether it is safe method. We theoretically study their interplay to issue a loan (Feldman et al., 2015), she begins by auditing when agents operate independently or collaborate.

Recent legislation required AI platforms to provide APIs for regulators to assess their compliance with the law. Research has nevertheless shown that platforms can manipulate their API answers through fairwashing. Facing this threat for reliable auditing, this paper studies the benefits of the joint use of platform scraping and of APIs. In this setup, we elaborate on the use of scraping to detect manipulated answers: since fairwashing only manipulates API answers, exploiting scraps may reveal a manipulation. To abstract the wide range of specific API-scrap situations, we introduce a notion of proxy that captures the consistency an auditor might expect between both data sources. If the regulator has a good proxy of the consistency, then she can easily detect manipulation and even bypass the API to conduct her audit. On the other hand, without a good proxy, relying on the API is necessary, and the auditor cannot defend against fairwashing. We then simulate practical scenarios in which the auditor may mostly rely on the API to conveniently conduct the audit task, while maintaining her chances to detect a potential manipulation. To highlight the tension between the audit task and the API fairwashing detection task, we identify Pareto-optimal strategies in a practical audit scenario. We believe this research sets the stage for reliable audits in practical and manipulation-prone setups.