Towards Explainable RAG - Interpreting the Influence of Retrieved Passages on Generation: Human Oversight and Decision Accountability

Authors

  • Russell Warren Author
  • Trent Butler Author
  • Warren Gibson Author

Keywords:

reliable retrieval-augmented generation, human oversight and decision accountability, evidence synthesis, reproducibility, research evaluation

Abstract

Human oversight is meaningful when intervention points, responsibility, escalation paths, and the evidence available to decision makers are explicitly defined. This structured evidence review evaluates "Towards Explainable RAG: Interpreting the Influence of Retrieved Passages on Generation" alongside nine author-disjoint, topically matched publications in reliable retrieval-augmented generation. It compares construct definitions, evaluation choices, operating assumptions, and reported limitations instead of treating bibliographic similarity as empirical equivalence. Viewed through human oversight and decision accountability, the map separates claims supported by the available record from questions that still require full-text extraction, replication, or new experiments. The synthesis is interpretive rather than meta-analytic and therefore does not present a pooled effect estimate or a new causal result. The resulting agenda specifies reviewer authority, override logging, escalation procedures, and evaluation of automation bias.

References

  1. Sang, Y. (2025). Towards Explainable RAG: Interpreting the Influence of Retrieved Passages on Generation. 2025 4th International Conference on Robotics, Artificial Intelligence and Intelligent Control (RAIIC), 397-400. https://doi.org/10.1109/raiic65850.2025.11170170
  2. Hassan, S.-B., Abdullah,, & Abbas, M. (2026). Agentic Self-RAG: Multi-Agent Reasoning for Self-Correcting Retrieval-Augmented Generation. 2026 International Conference on IT and Industrial Technologies (ICIT), 1-6. https://doi.org/10.1109/icit68548.2026.11577708
  3. Fasolino, I. (2026). In RAG We Trust? Measuring Robustness of Retrieval-Augmented Generation Under Document Poisoning. . https://doi.org/10.32388/7hxkqe
  4. Samal, M.-P. (2026). A Theoretical Analysis of Self-Contained Retrieval-Augmented Generation with Small Language Models. . https://doi.org/10.36227/techrxiv.177219989.96070478/v1
  5. Pi, W. (2024). Efficient Information Retrieval and Response Generation with Retrieval-Augmented Generation (RAG). . https://doi.org/10.59350/q2pq3-0fv85
  6. Xue, X., Zhang, G., Jiang, L., & Liu, C. (2025). A Comparative Study of Retrieval-Augmented Generation, Graph Retrieval-Augmented Generation, and Fine-Tuned Large Language Models for Fire Engineering Knowledge Retrieval. . https://doi.org/10.2139/ssrn.5316632
  7. Bose, R. (2025). Introduction to Retrieval-Augmented Generation (RAG). Mastering Retrieval-Augmented Generation, 3-32. https://doi.org/10.1007/979-8-8688-1808-0_1
  8. Zhai, W. (2025). SAM-RAG: An Self-adaptive Framework for Multimodal Retrieval-Augmented Generation. 2025 International Joint Conference on Neural Networks (IJCNN), 1-8. https://doi.org/10.1109/ijcnn64981.2025.11227819
  9. Gudipati, S.-K., Mishra, N.-A., Ankam, G., Akkisetti, S.-R., Mohammed, A., & Ponugoti, S. (2026). CityCopilot-X: A Real-Time Explainability Panel for Retrieval-Augmented Generation (RAG). 2026 IEEE 5th International Conference on AI in Cybersecurity (ICAIC), 1-9. https://doi.org/10.1109/icaic67076.2026.11395791
  10. Kau, A. (2024). Understanding Retrieval Pitfalls: Challenges Faced by Retrieval Augmented Generation (RAG) models. . https://doi.org/10.59350/xcq3s-jvk04

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Published

2026-08-27

Issue

Section

Review Article