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.

在学习排序（LTR）中，为了确保排名结果的后验公平性，提出了一种新的目标函数，通过性能约束来最大化预期相关性。该方法在 LTR 框架中构建了一个组内公平的 Plackett-Luce 模型，并展示了在三个真实数据集上的实验证明了其相比 LTR 基线模型在相关性方面具有更好的公平性和性能。