When learning to rank from user interactions, search and recommender systems must address biases in user behavior to provide a high-quality ranking. One type of bias that has recently been studied in the ranking literature is when sensitive attributes, such as gender, have an impact on a user's judgment about an item's utility. For example, in a search for an expertise area, some users may be biased towards clicking on male candidates over female candidates. We call this type of bias group membership bias. Increasingly, we seek rankings that are fair to individuals and sensitive groups. Merit-based fairness measures rely on the estimated utility of the items. With group membership bias, the utility of the sensitive groups is under-estimated, hence, without correcting for this bias, a supposedly fair ranking is not truly fair. In this paper, first, we analyze the impact of group membership bias on ranking quality as well as merit-based fairness metrics and show that group membership bias can hurt both ranking and fairness. Then, we provide a correction method for group bias that is based on the assumption that the utility score of items in different groups comes from the same distribution. This assumption has two potential issues of sparsity and equality-instead-of-equity; we use an amortized approach to address these. We show that our correction method can consistently compensate for the negative impact of group membership bias on ranking quality and fairness metrics.

翻译：在利用用户交互进行排序学习时，搜索与推荐系统需应对用户行为中的偏见以提供高质量的排序结果。近期排序研究关注的一种偏见是，性别等敏感属性会影响用户对项目效用的判断。例如，在专业领域搜索中，部分用户可能倾向于点击男性候选人而非女性候选人。我们将此类偏见称为群体归属偏见。当前，我们越来越追求对个人和敏感群体公平的排序。基于能力的公平性指标依赖于项目效用的估计值。在群体归属偏见的影响下，敏感群体的效用被低估，因此若未纠正此类偏见，看似公平的排序实则并不公平。本文首先分析了群体归属偏见对排序质量及基于能力的公平性指标的影响，表明该偏见会同时损害排序效果与公平性。随后，我们提出一种基于不同群体项目效用分数同分布假设的群体偏见纠正方法。该假设存在稀疏性与"平等而非公平"两大潜在问题,我们采用分摊方法加以解决。实验证明，我们的纠正方法能持续补偿群体归属偏见对排序质量与公平性指标的负面影响。