Societal biases resonate in the retrieved contents of information retrieval
(IR) systems, resulting in reinforcing existing stereotypes. Approaching this
issue requires established measures of fairness in respect to the
representation of various social groups in retrieval results, as well as
methods to mitigate such biases, particularly in the light of the advances in
deep ranking models. In this work, we first provide a novel framework to
measure the fairness in the retrieved text contents of ranking models.
Introducing a ranker-agnostic measurement, the framework also enables the
disentanglement of the effect on fairness of collection from that of rankers.
To mitigate these biases, we propose AdvBert, a ranking model achieved by
adapting adversarial bias mitigation for IR, which jointly learns to predict
relevance and remove protected attributes. We conduct experiments on two
passage retrieval collections (MSMARCO Passage Re-ranking and TREC Deep
Learning 2019 Passage Re-ranking), which we extend by fairness annotations of a
selected subset of queries regarding gender attributes. Our results on the
MSMARCO benchmark show that, (1) all ranking models are less fair in comparison
with ranker-agnostic baselines, and (2) the fairness of Bert rankers
significantly improves when using the proposed AdvBert models. Lastly, we
investigate the trade-off between fairness and utility, showing that we can
maintain the significant improvements in fairness without any significant loss
in utility.

本研究提出了一种新的框架来度量排名模型中检索文本内容的公平性，并使用针对信息检索的对抗性公平性缓解方法提出了一种排名模型 (AdvBert)，该模型联合学习以预测相关性并删除受保护特征，为信息检索中存在的偏见问题提供了有效的解决方案。实验结果表明，与基线算法相比，所有排名模型的公平性较低，使用提出的 AdvBert 模型可以显著提高 Bert 排名器的公平性，同时在不损失实用性的情况下保持显着的公平性改善。