Using the FiND world, a hypothetical fair world where protected attributes have no causal effect on the target, the authors show that fairness metrics become compatible and fairness aligns with accuracy, and that pre-processing methods can approximate this world in practice.
Fairness Through Regularization for Learning to Rank
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abstract
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.
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Overcoming Fairness Trade-offs via Pre-processing: A Causal Perspective
Using the FiND world, a hypothetical fair world where protected attributes have no causal effect on the target, the authors show that fairness metrics become compatible and fairness aligns with accuracy, and that pre-processing methods can approximate this world in practice.