The fast spreading adoption of machine learning (ML) by companies across industries poses significant regulatory challenges. One such challenge is scalability: how can regulatory bodies efficiently audit these ML models, ensuring that they are fair? In this paper, we initiate the study of query-based auditing algorithms that can estimate the demographic parity of ML models in a query-efficient manner. We propose an optimal deterministic algorithm, as well as a practical randomized, oracle-efficient algorithm with comparable guarantees. Furthermore, we make inroads into understanding the optimal query complexity of randomized active fairness estimation algorithms. Our first exploration of active fairness estimation aims to put AI governance on firmer theoretical foundations.

翻译：跨行业公司迅速推广机器学习(ML)给监管带来了巨大的挑战。 此类挑战之一是可扩展性:监管机构如何有效审计这些ML模型,确保这些模型公平? 在本文中,我们启动了基于询问的审计算法研究,该算法可以以查询效率高的方式估计ML模型的人口均等。我们提出了最佳确定算法,以及具有可比保障的实用随机、高压算法。此外,我们接近于理解随机化主动公平估算算法的最佳查询复杂性。我们第一次对积极公平估算的探索旨在将AI治理置于更坚实的理论基础之上。