In learning-to-rank (LTR), optimizing only the relevance (or the expected ranking utility) can cause representational harm to certain categories of items. Moreover, if there is implicit bias in the relevance scores, LTR models may fail to optimize for true relevance. Previous works have proposed efficient algorithms to train stochastic ranking models that achieve fairness of exposure to the groups ex-ante (or, in expectation), which may not guarantee representation fairness to the groups ex-post, that is, after realizing a ranking from the stochastic ranking model. Typically, ex-post fairness is achieved by post-processing, but previous work does not train stochastic ranking models that are aware of this post-processing. In this paper, we propose a novel objective that maximizes expected relevance only over those rankings that satisfy given representation constraints to ensure ex-post fairness. Building upon recent work on an efficient sampler for ex-post group-fair rankings, we propose a group-fair Plackett-Luce model and show that it can be efficiently optimized for our objective in the LTR framework. Experiments on three real-world datasets show that our group-fair algorithm guarantees fairness alongside usually having better relevance compared to the LTR baselines. In addition, our algorithm also achieves better relevance than post-processing baselines, which also ensures ex-post fairness. Further, when implicit bias is injected into the training data, our algorithm typically outperforms existing LTR baselines in relevance.

翻译：在排序学习（LTR）中，仅优化相关性（或期望排序效用）可能导致对某些类别的物品造成代表性损害。此外，若相关性评分存在隐性偏差，LTR模型可能无法优化真实相关性。已有研究提出高效算法训练随机排序模型，以实现群组曝光的事前公平性（即期望意义上的公平性），但这无法保证群组的事后代表性公平性——即从随机排序模型实现具体排序结果后。通常，事后公平性通过后处理实现，但先前工作未训练感知此类后处理的随机排序模型。本文提出一种新型目标函数，仅对满足给定代表性约束以确保事后公平性的排序结果最大化期望相关性。基于近期提出的高效事后群组公平排序采样器，我们构建了群组公平Plackett-Luce模型，并证明其可在LTR框架下针对我们的目标高效优化。在三个真实数据集上的实验表明，我们的群组公平算法在保证公平性的同时，通常能获得优于LTR基线的相关性。此外，该算法在保证事后公平性的前提下，相比后处理基线实现了更好的相关性。而当训练数据被注入隐性偏差时，我们的算法通常在相关性上超越现有LTR基线。