Fair Top-k Query on Alpha-Fairness

Hao Liu1; Raymond Chi-Wing Wong1; Zheng Zhang2; Min Xie3; Bo Tang4

10.1109/ICDE60146.2024.00185

2024-05-16

1063-6382

979-8-3503-1716-9

2024 IEEE 40th International Conference on Data Engineering (ICDE)

13-16 May 2024

Utrecht, Netherlands

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.

其他

EI

1.The Hong Kong University of Science and Technology
2.Individual
3.Shenzhen Institute of Computing Sciences
4.Southern University of Science and Technology

Hao Liu,Raymond Chi-Wing Wong,Zheng Zhang,et al. Fair Top-k Query on Alpha-Fairness[C],2024.