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Fairness for Robust Learning to Rank
Omid Memarrast · Ashkan Rezaei · Rizal Fathony · Brian Ziebart

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.

Author Information

Omid Memarrast (University of Illinois, Chicago)
Ashkan Rezaei (University of Illinois at Chicago)
Rizal Fathony (Carnegie Mellon University)
Brian Ziebart (University of Illinois at Chicago)

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