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.

本文研究了 AI 平台的审计问题，提出了平台抓取和 API 联合使用的方法，通过代理检测 API 回答的篡改，从而提高审计可靠性。

面对公正洗白审计，API 的相关性

On the relevance of APIs facing fairwashed audits

Black-box explanation is the problem of explaining how a machine learning
model -- whose internal logic is hidden to the auditor and generally complex --
produces its outcomes. Current approaches for solving this problem include
model explanation, outcome explanation as well as model inspection. While these
techniques can be beneficial by providing interpretability, they can be used in
a negative manner to perform fairwashing, which we define as promoting the
false perception that a machine learning model respects some ethical values. In
particular, we demonstrate that it is possible to systematically rationalize
decisions taken by an unfair black-box model using the model explanation as
well as the outcome explanation approaches with a given fairness metric. Our
solution, LaundryML, is based on a regularized rule list enumeration algorithm
whose objective is to search for fair rule lists approximating an unfair
black-box model. We empirically evaluate our rationalization technique on
black-box models trained on real-world datasets and show that one can obtain
rule lists with high fidelity to the black-box model while being considerably
less unfair at the same time.

研究了黑匣子解释问题，利用 LaundryML 算法演示如何以一定的公平指标系统地合理化黑匣子机器学习模型的不公平决策，可提供高效的规则列表，同时满足高度匹配和更少的不公平性。