On the Problem of Underranking in Group-Fair Ranking

Sruthi Gorantla, Amit Deshpande, Anand Louis
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:3777-3787, 2021.

Abstract

Bias in ranking systems, especially among the top ranks, can worsen social and economic inequalities, polarize opinions, and reinforce stereotypes. On the other hand, a bias correction for minority groups can cause more harm if perceived as favoring group-fair outcomes over meritocracy. Most group-fair ranking algorithms post-process a given ranking and output a group-fair ranking. In this paper, we formulate the problem of underranking in group-fair rankings based on how close the group-fair rank of each item is to its original rank, and prove a lower bound on the trade-off achievable for simultaneous underranking and group fairness in ranking. We give a fair ranking algorithm that takes any given ranking and outputs another ranking with simultaneous underranking and group fairness guarantees comparable to the lower bound we prove. Our experimental results confirm the theoretical trade-off between underranking and group fairness, and also show that our algorithm achieves the best of both when compared to the state-of-the-art baselines.

Cite this Paper

BibTeX
@InProceedings{pmlr-v139-gorantla21a, title = {On the Problem of Underranking in Group-Fair Ranking}, author = {Gorantla, Sruthi and Deshpande, Amit and Louis, Anand}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {3777--3787}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month = {18--24 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v139/gorantla21a/gorantla21a.pdf}, url = {https://proceedings.mlr.press/v139/gorantla21a.html}, abstract = {Bias in ranking systems, especially among the top ranks, can worsen social and economic inequalities, polarize opinions, and reinforce stereotypes. On the other hand, a bias correction for minority groups can cause more harm if perceived as favoring group-fair outcomes over meritocracy. Most group-fair ranking algorithms post-process a given ranking and output a group-fair ranking. In this paper, we formulate the problem of underranking in group-fair rankings based on how close the group-fair rank of each item is to its original rank, and prove a lower bound on the trade-off achievable for simultaneous underranking and group fairness in ranking. We give a fair ranking algorithm that takes any given ranking and outputs another ranking with simultaneous underranking and group fairness guarantees comparable to the lower bound we prove. Our experimental results confirm the theoretical trade-off between underranking and group fairness, and also show that our algorithm achieves the best of both when compared to the state-of-the-art baselines.} }
Endnote
%0 Conference Paper %T On the Problem of Underranking in Group-Fair Ranking %A Sruthi Gorantla %A Amit Deshpande %A Anand Louis %B Proceedings of the 38th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Marina Meila %E Tong Zhang %F pmlr-v139-gorantla21a %I PMLR %P 3777--3787 %U https://proceedings.mlr.press/v139/gorantla21a.html %V 139 %X Bias in ranking systems, especially among the top ranks, can worsen social and economic inequalities, polarize opinions, and reinforce stereotypes. On the other hand, a bias correction for minority groups can cause more harm if perceived as favoring group-fair outcomes over meritocracy. Most group-fair ranking algorithms post-process a given ranking and output a group-fair ranking. In this paper, we formulate the problem of underranking in group-fair rankings based on how close the group-fair rank of each item is to its original rank, and prove a lower bound on the trade-off achievable for simultaneous underranking and group fairness in ranking. We give a fair ranking algorithm that takes any given ranking and outputs another ranking with simultaneous underranking and group fairness guarantees comparable to the lower bound we prove. Our experimental results confirm the theoretical trade-off between underranking and group fairness, and also show that our algorithm achieves the best of both when compared to the state-of-the-art baselines.
APA
Gorantla, S., Deshpande, A. & Louis, A.. (2021). On the Problem of Underranking in Group-Fair Ranking. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research 139:3777-3787 Available from https://proceedings.mlr.press/v139/gorantla21a.html.

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