Jade Garcia Bourrée

Martijn de Vos, Akash Dhasade, Jade Garcia Bourrée et al.

Existing work in fairness auditing assumes that each audit is performed independently. In this paper, we consider multiple agents working together, each auditing the same platform for different tasks. Agents have two levers: their collaboration strategy, with or without coordination beforehand, and their strategy for sampling appropriate data points. We theoretically compare the interplay of these levers. Our main findings are that (i) collaboration is generally beneficial for accurate audits, (ii) basic sampling methods often prove to be effective, and (iii) counter-intuitively, extensive coordination on queries often deteriorates audits accuracy as the number of agents increases. Experiments on three large datasets confirm our theoretical results. Our findings motivate collaboration during fairness audits of platforms that use ML models for decision-making.

Jade Garcia Bourrée, Hadrien Lautraite, Sébastien Gambs et al.

The emergence of AI legislation has increased the need to assess the ethical compliance of high-risk AI systems. Traditional auditing methods rely on platforms' application programming interfaces (APIs), where responses to queries are examined through the lens of fairness requirements. However, such approaches put a significant burden on platforms, as they are forced to maintain APIs while ensuring privacy, facing the possibility of data leaks. This lack of proper collaboration between the two parties, in turn, causes a significant challenge to the auditor, who is subject to estimation bias as they are unaware of the data distribution of the platform. To address these two issues, we present P2NIA, a novel auditing scheme that proposes a mutually beneficial collaboration for both the auditor and the platform. Extensive experiments demonstrate P2NIA's effectiveness in addressing both issues. In summary, our work introduces a privacy-preserving and non-iterative audit scheme that enhances fairness assessments using synthetic or local data, avoiding the challenges associated with traditional API-based audits.

Jade Garcia Bourrée, Augustin Godinot, Martijn De Vos et al.

Among the many technical challenges to enforcing AI regulations, one crucial yet underexplored problem is the risk of audit manipulation. This manipulation occurs when a platform deliberately alters its answers to a regulator to pass an audit without modifying its answers to other users. In this paper, we introduce a novel approach to manipulation-proof auditing by taking into account the auditor's prior knowledge of the task solved by the platform. We first demonstrate that regulators must not rely on public priors (e.g. a public dataset), as platforms could easily fool the auditor in such cases. We then formally establish the conditions under which an auditor can prevent audit manipulations using prior knowledge about the ground truth. Finally, our experiments with two standard datasets illustrate the maximum level of unfairness a platform can hide before being detected as malicious. Our formalization and generalization of manipulation-proof auditing with a prior opens up new research directions for more robust fairness audits.

Jade Garcia Bourrée, Erwan Le Merrer, Gilles Tredan et al.

Recent legislation requires online platforms to provide dedicated APIs to assess the compliance of their decision-making algorithms with the law. Research has nevertheless shown that the auditors of such platforms are prone to manipulation (a practice referred to as \textit{fairwashing}). To address this salient problem, recent work has considered audits under the assumption of partial knowledge of the platform's internal mechanisms. In this paper, we propose a more pragmatic approach with the \textit{Two-Source Audit} setup: while still leveraging the API, we advocate for the adjunction of a second source of data to both perform the audit of a platform and the detection of fairwashing attempts. Our method is based on identifying discrepancies between the two data sources, using data proxies at use in the fairness literature. We formally demonstrate the conditions for success in this fairwashing mitigation task. We then validate our method empirically, demonstrating that Two-Source Audits can achieve a Pareto-optimal balance between the two objectives. We believe this paper sets the stage for reliable audits in manipulation-prone setups, under mild assumptions.