Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement

TL;DR

This paper introduces a novel LLM-based approach and a new fairness metric, CWEx, to better detect, measure, and analyze gender bias in passage ranking systems, supported by annotated datasets and extensive experiments.

Contribution

It presents a new gender bias detection method using LLMs, a novel fairness metric CWEx, and annotated datasets to improve bias evaluation in IR systems.

Findings

CWEx offers more detailed fairness evaluation than previous metrics.

The proposed method aligns better with human judgments (58.77% Cohen's Kappa).

The approach effectively distinguishes gender bias in ranking models.

Abstract

The presence of social biases in Natural Language Processing (NLP) and Information Retrieval (IR) systems is an ongoing challenge, which underlines the importance of developing robust approaches to identifying and evaluating such biases. In this paper, we aim to address this issue by leveraging Large Language Models (LLMs) to detect and measure gender bias in passage ranking. Existing gender fairness metrics rely on lexical- and frequency-based measures, leading to various limitations, e.g., missing subtle gender disparities. Building on our LLM-based gender bias detection method, we introduce a novel gender fairness metric, named Class-wise Weighted Exposure (CWEx), aiming to address existing limitations. To measure the effectiveness of our proposed metric and study LLMs' effectiveness in detecting gender bias, we annotate a subset of the MS MARCO Passage Ranking collection and release our new gender bias collection, called MSMGenderBias, to foster future research in this area. Our extensive experimental results on various ranking models show that our proposed metric offers a more detailed evaluation of fairness compared to previous metrics, with improved alignment to human labels (58.77% for Grep-BiasIR, and 18.51% for MSMGenderBias, measured using Cohen's Kappa agreement), effectively distinguishing gender bias in ranking. By integrating LLM-driven bias detection, an improved fairness metric, and gender bias annotations for an established dataset, this work provides a more robust framework for analyzing and mitigating bias in IR systems.