AISyst: AI‐Powered Interactive Visual System to Assist With Fidelity Assessment of Synthetic Tabular Data

Abstract

Evaluating synthetic data produced by generative models remains a critical challenge in sensitive domains such as healthcare and finance. Ensuring that such data is ‘faithful’ to real data is essential for downstream applications and decision-making, including regulatory compliance. This paper introduces an AI-powered interactive visual system—AISyst—designed to assess the fidelity of synthetic tabular datasets. The system supports multilevel comparisons with real datasets, spanning multivariate resemblance analyses based on dimensionality reduction through suitable two-dimensional projections, bivariate correlation and univariate similarity. AISyst also integrates an AI assistant by leveraging state-of-the-art large language models to summarize key findings and generate suggestions for improving synthetic data generation models. We validated the capabilities of AISyst through three case studies, supported by feedback from industrial AI experts who endorsed its broader deployment.