Exploring Trust and Transparency in Retrieval-Augmented Generation for Domain Experts
Abstract
The increasing adoption of Retrieval-Augmented Generation (RAG) systems in domain-specific applications raises critical challenges around trust and transparency, particularly for domain experts who possess deep subject knowledge but limited technical familiarity with AI systems. This paper investigates the design and evaluation of a user-centric RAG-based application tailored for financial equity research, incorporating features such as confidence indicators, source transparency, and user control mechanisms. Through a user study with 50 financial professionals, we found that while confidence scores provided useful context, they alone did not significantly enhance trust. In contrast, transparency features, including source attribution and highlighting document sections used in responses, substantially improved trust and user understanding. Participants also expressed a strong preference for greater control over source documents, such as filtering trusted reports and interacting with specific data. This work provides actionable insights for designing RAG systems that support trust, transparency, and control in high-stakes decision-making domains.
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.