The Impact of Group Membership Bias on the Quality and Fairness of Exposure in Ranking

TL;DR

This paper investigates how group membership bias affects ranking quality and fairness, analyzes its impact, and proposes a correction method to improve fairness and ranking performance.

Contribution

It introduces a correction method for group membership bias in ranking systems, addressing issues of sparsity and distributional assumptions to enhance fairness and quality.

Findings

Group membership bias harms ranking quality and fairness.

The proposed correction method effectively mitigates bias effects.

Corrected rankings show improved fairness metrics.

Abstract

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.