• Corpus ID: 245124106

Fairness for Robust Learning to Rank

  title={Fairness for Robust Learning to Rank},
  author={Omid Memarrast and Ashkan Rezaei and Rizal Fathony and Brian D. 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… 

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