Fairness Through Regularization for Learning to Rank
This addresses fairness concerns in ranking systems for end-users, but it is incremental as it adapts existing fairness notions to a new setting.
The paper tackles the problem of fairness in automated ranking systems by transferring fairness notions from binary classification to learning to rank, resulting in methods that improve ranking fairness substantially with no or only little loss of model quality.
Given the abundance of applications of ranking in recent years, addressing fairness concerns around automated ranking systems becomes necessary for increasing the trust among end-users. Previous work on fair ranking has mostly focused on application-specific fairness notions, often tailored to online advertising, and it rarely considers learning as part of the process. In this work, we show how to transfer numerous fairness notions from binary classification to a learning to rank setting. Our formalism allows us to design methods for incorporating fairness objectives with provable generalization guarantees. An extensive experimental evaluation shows that our method can improve ranking fairness substantially with no or only little loss of model quality.