Related papers: Fairness for Robust Learning to Rank

Fairness for Robust Learning to Rank

URL: http://arxiv.org/abs/2112.06288v1
Date: Sun, 12 Dec 2021 17:56:56 GMT
Title: Fairness for Robust Learning to Rank
Authors: Omid Memarrast, Ashkan Rezaei, Rizal Fathony, Brian Ziebart
Abstract summary: We derive a new ranking system based on the first principles of distributional robustness. We show that our approach provides better utility for highly fair rankings than existing baseline methods.
Score: 8.019491256870557
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: While conventional ranking systems focus solely on maximizing the utility of the ranked items to users, fairness-aware ranking systems additionally try to balance the exposure for different protected attributes such as gender or race. To achieve this type of group fairness for ranking, we derive a new ranking system based on the first principles of distributional robustness. We formulate a minimax game between a player choosing a distribution over rankings to maximize utility while satisfying fairness constraints against an adversary seeking to minimize utility while matching statistics of the training data. We show that our approach provides better utility for highly fair rankings than existing baseline methods.

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