Towards automated data analysis: A guided framework for LLM-based risk estimation

Researchers propose a new framework that combines Large Language Models with human supervision for automated dataset risk estimation. The approach aims to address limitations of manual auditing and AI hallucinations by having LLMs identify database properties and generate analysis code under human guidance.

• →New framework integrates LLMs with human supervision for automated dataset risk analysis.
• →Current manual auditing methods are time-consuming while fully automated AI analysis suffers from hallucinations.
• →The system uses LLMs to identify semantic and structural properties in database schemas and propose clustering techniques.
• →Human supervisors guide the model and ensure process integrity and alignment with objectives.
• →A proof of concept demonstrates the framework's feasibility for meaningful risk assessment results.