Data Augmentation via Diffusion Model to Enhance AI Fairness

TL;DR

This paper investigates using diffusion models, specifically Tab-DDPM, to generate synthetic tabular data for data augmentation, aiming to improve fairness in AI binary classification tasks.

Contribution

It introduces a novel application of diffusion models for synthetic tabular data generation to enhance AI fairness, combined with reweighting techniques for better results.

Findings

Synthetic data from Tab-DDPM improves fairness metrics

Reweighting samples further enhances fairness outcomes

Multiple ML models benefit from augmented data in fairness improvements

Abstract

AI fairness seeks to improve the transparency and explainability of AI systems by ensuring that their outcomes genuinely reflect the best interests of users. Data augmentation, which involves generating synthetic data from existing datasets, has gained significant attention as a solution to data scarcity. In particular, diffusion models have become a powerful technique for generating synthetic data, especially in fields like computer vision. This paper explores the potential of diffusion models to generate synthetic tabular data to improve AI fairness. The Tabular Denoising Diffusion Probabilistic Model (Tab-DDPM), a diffusion model adaptable to any tabular dataset and capable of handling various feature types, was utilized with different amounts of generated data for data augmentation. Additionally, reweighting samples from AIF360 was employed to further enhance AI fairness. Five traditional machine learning models-Decision Tree (DT), Gaussian Naive Bayes (GNB), K-Nearest Neighbors (KNN), Logistic Regression (LR), and Random Forest (RF)-were used to validate the proposed approach. Experimental results demonstrate that the synthetic data generated by Tab-DDPM improves fairness in binary classification.