Fairness in Ranking: Robustness through Randomization without the Protected Attribute
This addresses fairness challenges in ranking applications like online advertising and HR automation, offering a solution for scenarios where protected attributes are unavailable, though it is incremental in building on existing methods.
The paper tackles fairness in ranking without requiring protected attributes, proposing a randomized post-processing method that shows robustness across multiple fairness measures and improves on baseline NDCG in numerical studies.
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.