Fair Top-k Query on Alpha-Fairness
Hao Liu, Raymond Chi-Wing Wong, Zheng Zhang, Min Xie, Bo Tang · 2024
The traditional top-k query was proposed to obtain a small subset from the database according to the user preference, which is explicitly expressed as a ranking scheme (i.e., utility function). However, a poorly-designed utility function may create discrimination, which in turn may cause harm to minority groups, e.g., women and ethnic minorities, and thus, fairness is becoming increasingly important in many situations, e.g., hiring and admission decisions. Motivated by this, we study fair ranking to alleviate discrimination. We design a fairness model, called α-fairness, to quantify the fairness of utility functions. We propose an efficient exact framework with a basic implementation and an improved implementation to find the fairest utility function with the minimum modification penalty. We conducted extensive experiments on both real and synthetic datasets to demonstrate our effectiveness and efficiency compared with the prior studies.