论文标题
主动公平审核
Active Fairness Auditing
论文作者
论文摘要
公司跨行业对机器学习（ML）的快速传播采用了重大的监管挑战。一个这样的挑战就是可伸缩性：监管机构如何有效地审核这些ML模型，以确保它们是公平的？在本文中，我们启动基于查询的审计算法的研究，这些算法可以以查询有效的方式估算ML模型的人口统计学率。我们提出了一种最佳的确定性算法，以及具有可比保证的实用随机，甲骨文效率的算法。此外，我们进一步了解了随机活动公平估计算法的最佳查询复杂性。我们对主动公平估计的首次探索旨在将AI治理置于更坚定的理论基础上。
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.
