Fairness in Ranking: Robustness through Randomization without the Protected Attribute
<|reference_start|>Fairness in Ranking: Robustness through Randomization without the Protected Attribute: 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.<|reference_end|>
@article{kliachkin2024fairness,
    title={Fairness in Ranking: Robustness through Randomization without the
  Protected Attribute},
    author={Andrii Kliachkin and Eleni Psaroudaki and Jakub Marecek and Dimitris
  Fotakis},
    journal={arXiv preprint arXiv:2403.19419},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.19419},
    primaryClass={cs.LG cs.AI cs.CY}
}