Gender disentangled representation learning in neural rankers

Shirin Seyedsalehi, Sara Salamat, Negar Arabzadeh, Sajad Ebrahimi, Morteza Zihayat, Ebrahim Bagheri
January 2025

Abstract

This paper presents gender disentangled representation learning approaches for neural rankers, addressing bias issues in information retrieval systems.

Type
Journal article
Publication
Machine Learning

This work introduces gender disentanglement techniques for reducing bias in neural ranking systems.

Gender Disentanglement Representation Learning Neural Rankers Bias Mitigation Information Retrieval
Shirin Seyedsalehi
PhD Student (Alumni)

Shirin Seyedsalehi is a former PhD student and alumna of the Human-Centered Machine Intelligence Lab.

Morteza Zihayat
Morteza Zihayat
Principal Investigator

Dr. Morteza Zihayat is a Canada Research Chair (CRC) in Human-Centered AI and Associate Professor at Toronto Metropolitan University, Faculty of Engineering and Architectural Science. He also holds appointments as Adjunct Associate Professor at the University of Waterloo (Management Sciences) and IBM Faculty Fellow at IBM Centre for Advanced Studies. He is the Director of the Human-Centered Machine Intelligence Lab.