Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models

TL;DR

This paper presents a novel diffusion-based method for generating fair synthetic tabular data that reduces bias and improves fairness metrics while maintaining high data quality.

Contribution

We introduce a sensitive-guided diffusion model that enhances fairness in synthetic tabular data generation, outperforming existing methods on key fairness metrics.

Findings

Effectively mitigates bias in training data

Outperforms existing methods on fairness metrics

Maintains high quality of generated samples

Abstract

Diffusion models have emerged as a robust framework for various generative tasks, including tabular data synthesis. However, current tabular diffusion models tend to inherit bias in the training dataset and generate biased synthetic data, which may influence discriminatory actions. In this research, we introduce a novel tabular diffusion model that incorporates sensitive guidance to generate fair synthetic data with balanced joint distributions of the target label and sensitive attributes, such as sex and race. The empirical results demonstrate that our method effectively mitigates bias in training data while maintaining the quality of the generated samples. Furthermore, we provide evidence that our approach outperforms existing methods for synthesizing tabular data on fairness metrics such as demographic parity ratio and equalized odds ratio, achieving improvements of over $10\%$. Our implementation is available at https://github.com/comp-well-org/fair-tab-diffusion.