How Fair is Fairness-aware Representative Ranking and Methods for Fair Ranking

TL;DR

This paper examines biases in fairness-aware ranking algorithms, defining individual and group unfairness, and proposes methods to generate fair rankings considering known or unknown representation ratios, supported by simulation results.

Contribution

It introduces definitions of individual and group unfairness in fair ranking and proposes methods to achieve ideal fairness under various representation scenarios.

Findings

Bias exists in fairness-aware ranking for individuals and groups.

Proposed methods can generate fair rankings when representation ratios are known or unknown.

Simulation results quantify fairness improvements in the proposed solutions.

Abstract

Rankings of people and items has been highly used in selection-making, match-making, and recommendation algorithms that have been deployed on ranging of platforms from employment websites to searching tools. The ranking position of a candidate affects the amount of opportunities received by the ranked candidate. It has been observed in several works that the ranking of candidates based on their score can be biased for candidates belonging to the minority community. In recent works, the fairness-aware representative ranking was proposed for computing fairness-aware re-ranking of results. The proposed algorithm achieves the desired distribution of top-ranked results with respect to one or more protected attributes. In this work, we highlight the bias in fairness-aware representative ranking for an individual as well as for a group if the group is sub-active on the platform. We define individual unfairness and group unfairness and propose methods to generate ideal individual and group fair representative ranking if the universal representation ratio is known or unknown. The simulation results show the quantified analysis of fairness in the proposed solutions. The paper is concluded with open challenges and further directions.