Cited By
View all- Saxena AFletcher GPechenizkiy M(2024)FairSNA: Algorithmic Fairness in Social Network AnalysisACM Computing Surveys10.1145/365371156:8(1-45)Online publication date: 26-Apr-2024
How Fair is Fairness-aware Representative Ranking?
Pages 161 - 165
Abstract
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 and for a group if the group is sub-active on the platform. We define individual unfairness and group unfairness from two different perspectives. We further propose methods to generate ideal individual and group fair representative ranking if the universal representation ratio is known. The paper is concluded with open challenges and further directions.
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April 2021
726 pages
ISBN:9781450383134
DOI:10.1145/3442442
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Published: 03 June 2021
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View all- Saxena AFletcher GPechenizkiy M(2024)FairSNA: Algorithmic Fairness in Social Network AnalysisACM Computing Surveys10.1145/365371156:8(1-45)Online publication date: 26-Apr-2024
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