Persons
Andrii Kliachkin, MSc.
All publications
Fairness in Ranking: Robustness through Randomization without the Protected Attribute
- Authors: Andrii Kliachkin, MSc., Psaroudaki, E., Mgr. Jakub Mareček, Ph.D., Fotakis, D.
- Publication: 2024 IEEE 40th International Conference on Data Engineering Workshops (ICDEW). New York: Institute of Electrical and Electronics Engineers, 2024. p. 201-208. ISSN 1943-2895. ISBN 979-8-3503-8404-8.
- Year: 2024
- DOI: 10.1109/ICDEW61823.2024.00032
- Link: https://doi.org/10.1109/ICDEW61823.2024.00032
- Department: Artificial Intelligence Center
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Annotation:
There has been great interest in fairness in machine learning, especially in relation to classification problems. In ranking-related problems, such as in online advertising, recommender systems, and HR automation, much work on fairness remains to be done. Two complications arise: first, the protected attribute may not be available in many applications. Second, there are multiple measures of fairness of rankings, and optimization-based methods utilizing a single measure of fairness of rankings may produce rankings that are unfair with respect to other measures. In this work, we propose a randomized method for post-processing rankings, which do not require the availability of the protected attribute. In an extensive numerical study, we show the robustness of our methods with respect to P-Fairness and effectiveness with respect to Normalized Discounted Cumulative Gain (NDCG) from the baseline ranking, improving on previously proposed methods.