Towards automated data analysis: A guided framework for LLM-based risk estimation
#large language models #risk estimation #automated data analysis #AI framework #data-driven assessment
📌 Key Takeaways
- Researchers propose a guided framework for automating risk estimation using large language models (LLMs).
- The framework aims to enhance data analysis by integrating LLMs to assess and quantify risks systematically.
- It addresses challenges in traditional risk analysis by leveraging AI for more efficient and scalable evaluations.
- The approach is designed to be adaptable across various domains requiring data-driven risk assessment.
📖 Full Retelling
arXiv:2603.04631v1 Announce Type: new
Abstract: Large Language Models (LLMs) are increasingly integrated into critical decision-making pipelines, a trend that raises the demand for robust and automated data analysis. Current approaches to dataset risk analysis are limited to manual auditing methods which involve time-consuming and complex tasks, whereas fully automated analysis based on Artificial Intelligence (AI) suffers from hallucinations and issues stemming from AI alignment. To this end,
🏷️ Themes
AI Automation, Risk Analysis
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--> Computer Science > Artificial Intelligence arXiv:2603.04631 [Submitted on 4 Mar 2026] Title: Towards automated data analysis: A guided framework for LLM-based risk estimation Authors: Panteleimon Rodis View a PDF of the paper titled Towards automated data analysis: A guided framework for LLM-based risk estimation, by Panteleimon Rodis View PDF HTML Abstract: Large Language Models are increasingly integrated into critical decision-making pipelines, a trend that raises the demand for robust and automated data analysis. Current approaches to dataset risk analysis are limited to manual auditing methods which involve time-consuming and complex tasks, whereas fully automated analysis based on Artificial Intelligence suffers from hallucinations and issues stemming from AI alignment. To this end, this work proposes a framework for dataset risk estimation that integrates Generative AI under human guidance and supervision, aiming to set the foundations for a future automated risk analysis paradigm. Our approach utilizes LLMs to identify semantic and structural properties in database schemata, subsequently propose clustering techniques, generate the code for them and finally interpret the produced results. The human supervisor guides the model on the desired analysis and ensures process integrity and alignment with the task's objectives. A proof of concept is presented to demonstrate the feasibility of the framework's utility in producing meaningful results in risk assessment tasks. Comments: Submitted for publication. Under review Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2603.04631 [cs.AI] (or arXiv:2603.04631v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2603.04631 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Panteleimon Rodis [ view email ] [v1] Wed, 4 Mar 2026 21:44:22 UTC (250 KB) Full-text links: Access Paper: View a PDF of the paper titled Towards automated data analysis: A guided fram...
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