Does RAG Introduce Unfairness in LLMs? Evaluating Fairness in Retrieval-Augmented Generation Systems

2024-09-29

International Conference on Computational Linguistics

AI Summary

This work systematically investigates fairness disparities—particularly along gender and geographic attributes—in retrieval-augmented generation (RAG) systems for open-domain question answering. Addressing the challenge of identifying and mitigating bias across RAG’s multi-component architecture, we introduce the first fairness evaluation framework specifically designed for RAG. It integrates scenario-based QA tasks, group-wise comparative statistics, retrieval distribution analysis, and fairness metrics for generated outputs, accompanied by a benchmark dataset and open-source implementation. Empirical results reveal that state-of-the-art RAG methods improve answer accuracy without enhancing fairness: both retrieval and generation stages exhibit significant performance gaps across demographic groups. Our framework establishes a reproducible methodological foundation and provides empirical evidence to advance fairness-aware RAG research.

Technology Category

• Computer Vision: Bias, Fairness & Privacy
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

Retrieval-Augmented Generation (RAG) has recently gained significant attention for its enhanced ability to integrate external knowledge sources into open-domain question answering (QA) tasks. However, it remains unclear how these models address fairness concerns, particularly with respect to sensitive attributes such as gender, geographic location, and other demographic factors. First, as language models evolve to prioritize utility, like improving exact match accuracy, fairness considerations may have been largely overlooked. Second, the complex, multi-component architecture of RAG methods poses challenges in identifying and mitigating biases, as each component is optimized for distinct objectives. In this paper, we aim to empirically evaluate fairness in several RAG methods. We propose a fairness evaluation framework tailored to RAG, using scenario-based questions and analyzing disparities across demographic attributes. Our experimental results indicate that, despite recent advances in utility-driven optimization, fairness issues persist in both the retrieval and generation stages. These findings underscore the need for targeted interventions to address fairness concerns throughout the RAG pipeline. The dataset and code used in this study are publicly available at this GitHub Repository https://github.com/elviswxy/RAG_fairness .

Problem

• Evaluates fairness in RAG systems regarding sensitive attributes
• Identifies bias challenges in multi-component RAG architectures
• Proposes interventions for fairness in retrieval and generation stages

Innovation

• Evaluates fairness in RAG systems empirically
• Proposes scenario-based fairness evaluation framework
• Analyzes demographic disparities in retrieval and generation