Towards FairRAG: Preventing Representational Harm in Retrieval-Augmented Generation by Enforcing Fair Exposure at Retrieval Time

As Large Language Model (LLM) integration has accelerated in high-stakes domains, model hallucination is a critical issue. Retrieval-augmented generation (RAG) is a technique for addressing hallucination; however, RAG's multi-component pipeline introduces vulnerabilities where biases can be introduced. This study considers two previously developed utility-focused ranking strategies (Standard and Stochastic) alongside two proposed exposure-aware approaches (Forced-Exposure and Representative Stochastic). Using the TREC 2022 Fair Ranking Dataset, which contains Wikipedia articles annotated as protected or non-protected, the LLM was asked to identify relevant articles with citations for four scenario-based Q&A prompts. The retrieval rankings and the generated outputs were evaluated for exposure bias and utility across all ranking methods. Overall, the Representative Stochastic ranker resulted in a statistically significant near-parity average exposure, acknowledging that relevance scores initially produced during retrieval are already shaped by representational bias, whereas the other rankers assume those scores are unbiased. Across all the methods of document ranking, generation demographic parity closely mirrored the exposure parity, reinforcing that representational bias in RAG systems is driven by retrieval and propagates to generation. These findings highlight that retrieval ranking is a critical point for mitigating downstream bias and propose a Representative Stochastic ranker that reintroduces fairness in RAG systems.