Fairness in Ranking: Robustness through Randomization without the
  Protected Attribute
Andrii Kliachkin Eleni Psaroudaki Jakub Marecek Dimitris Fotakis
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.