Bias-aware Fair Neural Ranking for Addressing Stereotypical Gender Biases

Research has shown that neural rankers can pick up and intensify gender biases. The expression of stereotypical gender biases in retrieval systems can lead to their reinforcement in users’ beliefs. As such, the objective of this paper is to propose a bias-aware fair ranker that explicitly incorporates a notion of gender bias and hence controls how bias is expressed in documents that are retrieved. The proposed approach is designed such that it learns the notion of relevance between the document and the query from the relevant sampled documents while incorporating the notion of gender bias by penalizing irrelevant biased sampled documents. We show that unlike the state of the art, our approach reduces bias while maintaining retrieval effectiveness over different query sets.