A Light-Weight Strategy for Restraining Gender Biases in Neural Rankers

Amin Bigdeli, Negar Arabzadeh, Shirin Seyedsalehi, Morteza Zihayat, Ebrahim Bagheri
January 2022

Abstract

This paper presents a light-weight strategy for restraining gender biases in neural rankers, providing efficient bias mitigation approaches.

Type
Conference paper
Publication
European Conference on Information Retrieval (ECIR)

This work introduces efficient light-weight approaches for mitigating gender biases in neural ranking systems.

Gender Bias Neural Rankers Light-Weight Methods 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.