How can we build recommender systems to take into account fairness? Real-world recommender systems are often composed of multiple models, built by multiple teams. However, most research on fairness focuses on improving fairness in a single model. Further, recent research on classification fairness has shown that combining multiple "fair" classifiers can still result in an "unfair" classification system. This presents a significant challenge: how do we understand and improve fairness in recommender systems composed of multiple components? In this paper, we study the compositionality of recommender fairness. We consider two recently proposed fairness ranking metrics: equality of exposure and pairwise ranking accuracy. While we show that fairness in recommendation is not guaranteed to compose, we provide theory for a set of conditions under which fairness of individual models does compose. We then present an analytical framework for both understanding whether a real system's signals can achieve compositional fairness, and improving which component would have the greatest impact on the fairness of the overall system. In addition to the theoretical results, we find on multiple datasets -- including a large-scale real-world recommender system -- that the overall system's end-to-end fairness is largely achievable by improving fairness in individual components.

翻译：我们如何建立建议系统以考虑到公平性?现实世界建议系统通常由多个团队建立的多种模式组成。然而,大多数关于公平性的研究都侧重于在单一模式中改善公平性。此外,最近关于分类公平性的研究表明,将多个“公平”分类者结合起来仍会导致“不公平”分类系统。这提出了一个重大挑战:我们如何理解由多个组成部分组成的建议系统并提高其公平性?在本文件中,我们研究了建议公平性的组成性。我们考虑了最近提出的两个公平等级标准:接触平等和对称排序准确性。虽然我们表明建议不能保证公平性,但我们为一套个人模式的公平性得以形成的条件提供了理论。然后我们提出了一个分析框架,以了解一个实际系统信号能否实现公平性,以及改进哪个组成部分将对整个系统的公平性产生最大影响。除了理论结果外,我们还发现多个数据集,包括一个大规模真实性建议系统,整个系统的端对端对端公平性在很大程度上可以通过提高个人组成部分的公平性来实现。