Towards Fair RAG: On the Impact of Fair Ranking in Retrieval-Augmented Generation
This paper presents the first comprehensive study of RAG systems that incorporate fairness-aware rankings, focusing on both ranking fairness and attribution fairness - ensuring equitable exposure of sources cited in the final text.
Abstract
Modern language models frequently include retrieval components to improve their outputs, giving rise to a growing number of retrieval-augmented generation (RAG) systems. Yet, most existing work in RAG has underemphasized fair ranking techniques and neglected the diverse interests of all stakeholders. In this paper, we present the first comprehensive study of RAG systems that incorporate fairness-aware rankings, focusing on both ranking fairness and attribution fairness - ensuring equitable exposure of sources cited in the final text. We specifically examine item-side fairness, i.e., whether retrieved documents receive balanced exposure, and assess how this affects both the system's overall performance and the eventual distribution of cited sources. Across twelve RAG models and seven tasks, we find that fairness-aware retrieval frequently retains or even improves ranking effectiveness and generation quality, countering the widespread belief that fairness compromises system performance. Moreover, we show that fair retrieval leads to more balanced attribution in the final responses, ensuring that the cited sources are credited more equitably. Our results underscore the importance of item-side fairness throughout both retrieval and generation phases, offering key insights for building more responsible and equitable RAG systems and illustrating promising avenues for future exploration in fair ranking and source attribution.