Gender disentangled representation learning in neural rankers

Shirin Seyedsalehi, Sara Salamat, Negar Arabzadeh, Sajad Ebrahimi, Morteza Zihayat, Ebrahim Bagheri

Abstract: Recent studies have demonstrated that while neural ranking methods excel in retrieval effectiveness, they also tend to amplify stereotypical biases, especially those related to gender. Current mitigation strategies often focus on adjusting training methods, like adversarial techniques or data balancing, but typically overlook explicit consideration of gender as an attribute. In this paper, we introduce a systematic approach that treats gender as a distinct component within neural ranker representations. Our neural disentanglement method separates content semantics from gender information, enabling the neural ranker to evaluate document relevance based on content alone, without the interference of gender-related information during retrieval. Our extensive experiments demonstrate that: (1) our disentanglement approach matches the effectiveness of baseline models and offers more consistent performance across queries of different gender affiliations; (2) isolating gender within the representations allows the neural ranker to produce an unbiased list of documents, not favoring any specific gender; and (3) the disentangled gender component effectively and concisely captures gender information independently from the semantic content.