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.

学习排序（LTR）是最广泛使用的机器学习应用程序之一。本文展示了如何将有效可解的公平排序模型整合到 LTR 模型的训练循环中，以在公平性、用户效用和运行效率之间取得有利的平衡。特别是，本文首次展示了如何通过 OOW 目标的约束优化进行反向传播，从而在集成的预测和决策模型中使用它们。

通过可微优化有序加权平均值学习公平排名策略

Learning Fair Ranking Policies via Differentiable Optimization of  Ordered Weighted Averages

Fuzzy rough set theory can be used as a tool for dealing with inconsistent
data when there is a gradual notion of indiscernibility between objects. It
does this by providing lower and upper approximations of concepts. In classical
fuzzy rough sets, the lower and upper approximations are determined using the
minimum and maximum operators, respectively. This is undesirable for machine
learning applications, since it makes these approximations sensitive to
outlying samples. To mitigate this problem, ordered weighted average (OWA)
based fuzzy rough sets were introduced. In this paper, we show how the
OWA-based approach can be interpreted intuitively in terms of vague
quantification, and then generalize it to Choquet-based fuzzy rough sets
(CFRS). This generalization maintains desirable theoretical properties, such as
duality and monotonicity. Furthermore, it provides more flexibility for machine
learning applications. In particular, we show that it enables the seamless
integration of outlier detection algorithms, to enhance the robustness of
machine learning algorithms based on fuzzy rough sets.

介绍了基于有序加权平均和 Choquet 的模糊粗糙集，在机器学习应用中能提供更大的灵活性，特别是在离群样本检测算法的无缝集成方面，以增强基于模糊粗糙集的机器学习算法的鲁棒性。