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Evidence record 6768 · automatically gathered

Who Benefits from RAG? The Role of Exposure, Utility and Attribution Bias

Large Language Models (LLMs) enhanced with Retrieval-Augmented Generation (RAG) have achieved substantial improvements in accuracy by grounding their responses in external documents that are relevant to the user's query. However, relatively little work has investigated the impact of RAG in terms of fairness. Particularly, it is not yet known if queries that are associated with certain groups within a fairness category systematically receive higher accuracy, or accuracy improvements in RAG system

Record details

Published: 25 March 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Finance, VC & PE
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (25 March 2026), “Who Benefits from RAG? The Role of Exposure, Utility and Attribution Bias,” evidence record 6768, https://ethics.ai/record/6768 (originally published by arXiv).

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