Debiasing Gender Bias in Information Retrieval Models

TL;DR

This paper addresses gender bias in information retrieval models by analyzing biases in pre-trained models and proposing a debiasing technique that promotes gender-balanced retrieval results, improving fairness without sacrificing performance.

Contribution

It introduces a novel debiasing method for IR models that reduces gender bias and enhances zero-shot retrieval performance using lightweight fine-tuning with adapters.

Findings

Lightweight fine-tuning with adapters improves zero-shot IR performance by nearly 20%.

Pre-trained models tend to retrieve more male-oriented articles, indicating gender bias.

The proposed debiasing technique effectively balances gender representation in retrieved articles.

Abstract

Biases in culture, gender, ethnicity, etc. have existed for decades and have affected many areas of human social interaction. These biases have been shown to impact machine learning (ML) models, and for natural language processing (NLP), this can have severe consequences for downstream tasks. Mitigating gender bias in information retrieval (IR) is important to avoid propagating stereotypes. In this work, we employ a dataset consisting of two components: (1) relevance of a document to a query and (2) "gender" of a document, in which pronouns are replaced by male, female, and neutral conjugations. We definitively show that pre-trained models for IR do not perform well in zero-shot retrieval tasks when full fine-tuning of a large pre-trained BERT encoder is performed and that lightweight fine-tuning performed with adapter networks improves zero-shot retrieval performance almost by 20% over baseline. We also illustrate that pre-trained models have gender biases that result in retrieved articles tending to be more often male than female. We overcome this by introducing a debiasing technique that penalizes the model when it prefers males over females, resulting in an effective model that retrieves articles in a balanced fashion across genders.