Towards automated data analysis: A guided framework for LLM-based risk estimation
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, 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 int
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, 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.
Executive Summary
This article proposes a guided framework for Large Language Model (LLM)-based risk estimation, which integrates Generative AI with human supervision to address the limitations of current manual auditing methods and automated AI analysis. The framework utilizes LLMs to identify semantic and structural properties in database schemata, proposes clustering techniques, generates code, and interprets results under human guidance. A proof of concept demonstrates the framework's feasibility in risk assessment tasks. While the approach offers a promising solution to automated data analysis, it requires further development and evaluation to ensure process integrity and alignment with task objectives. The framework's utility in producing meaningful results in risk assessment tasks is a significant contribution to the field.
Key Points
- ▸ The article proposes a guided framework for LLM-based risk estimation that integrates Generative AI with human supervision.
- ▸ The framework utilizes LLMs to identify semantic and structural properties in database schemata and proposes clustering techniques.
- ▸ Human supervision ensures process integrity and alignment with task objectives.
- ▸ A proof of concept demonstrates the framework's feasibility in risk assessment tasks.
Merits
Strength in Addressing Limitations
The proposed framework addresses the limitations of current manual auditing methods and automated AI analysis, offering a more efficient and effective solution to dataset risk estimation.
Demerits
Limited Evaluation
The framework requires further development and evaluation to ensure process integrity and alignment with task objectives, which may limit its immediate practical application.
Expert Commentary
The proposed framework represents a significant step forward in the development of automated data analysis, particularly in the context of risk estimation. However, its practical application will depend on the resolution of several key issues, including the development of more sophisticated LLMs, the refinement of human supervision protocols, and the establishment of robust evaluation frameworks. Furthermore, the framework's reliance on human oversight raises important questions about the role of human responsibility in decision-making processes, which will require careful consideration in the development of policy and regulatory frameworks.
Recommendations
- ✓ Further development and evaluation of the framework to ensure process integrity and alignment with task objectives.
- ✓ Investigation of the framework's potential to address AI alignment and bias concerns.
- ✓ Establishment of robust evaluation frameworks to assess the framework's utility and limitations.