Fairness in Ranking: Robustness through Randomization without the Protected Attribute

• URL: http://arxiv.org/abs/2403.19419v1
• Date: Thu, 28 Mar 2024 13:50:24 GMT
• Title: Fairness in Ranking: Robustness through Randomization without the Protected Attribute
• Authors: Andrii Kliachkin, Eleni Psaroudaki, Jakub Marecek, Dimitris Fotakis,
• Abstract summary: 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.
• Score: 15.086941303164375
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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