Learning to Rank (LTR) is one of the most widely used machine learning applications. It is a key component in platforms with profound societal impacts, including job search, healthcare information retrieval, and social media content feeds. Conventional LTR models have been shown to produce biases results, stimulating a discourse on how to address the disparities introduced by ranking systems that solely prioritize user relevance. However, while several models of fair learning to rank have been proposed, they suffer from deficiencies either in accuracy or efficiency, thus limiting their applicability to real-world ranking platforms. This paper shows how efficiently-solvable fair ranking models, based on the optimization of Ordered Weighted Average (OWA) functions, can be integrated into the training loop of an LTR model to achieve favorable balances between fairness, user utility, and runtime efficiency. In particular, this paper is the first to show how to backpropagate through constrained optimizations of OWA objectives, enabling their use in integrated prediction and decision models.

翻译：学习排序（LTR）是应用最广泛的机器学习技术之一，在具有深远社会影响的平台中扮演关键角色，包括求职搜索、医疗信息检索和社交媒体内容推送。研究表明，传统LTR模型会产生有偏差的结果，引发了关于如何解决仅优先考虑用户相关性的排序系统所导致的不公平现象的讨论。尽管已有多种公平排序学习模型被提出，但它们存在精度或效率方面的缺陷，限制了其在真实排序平台中的适用性。本文证明了基于有序加权平均（OWA）函数优化的高效可解公平排序模型，能够被集成到LTR模型的训练循环中，在公平性、用户效用和运行效率之间实现有利平衡。特别值得注意的是，本文首次展示了如何通过OWA目标的约束优化实现反向传播，从而支持其在预测与决策一体化模型中的应用。