  Bipartite ranking, which aims to learn a scoring function that ranks positive
individuals higher than negative ones from labeled data, is widely adopted in
various applications where sample prioritization is needed. Recently, there
have been rising concerns on whether the learned scoring function can cause
systematic disparity across different protected groups defined by sensitive
attributes. While there could be trade-off between fairness and performance, in
this paper we propose a model agnostic post-processing framework for balancing
them in the bipartite ranking scenario. Specifically, we maximize a weighted
sum of the utility and fairness by directly adjusting the relative ordering of
samples across groups. By formulating this problem as the identification of an
optimal warping path across different protected groups, we propose a
non-parametric method to search for such an optimal path through a dynamic
programming process. Our method is compatible with various classification
models and applicable to a variety of ranking fairness metrics. Comprehensive
experiments on a suite of benchmark data sets and two real-world patient
electronic health record repositories show that our method can achieve a great
balance between the algorithm utility and ranking fairness. Furthermore, we
experimentally verify the robustness of our method when faced with the fewer
training samples and the difference between training and testing ranking score
distributions.
