Disparate Impact in Synthetic Data Generation

2026-06-11

AI Summary

This work addresses the disparate impact arising in synthetic data generation, where limitations in model expressiveness, imbalanced group sampling ratios, and differential privacy mechanisms often lead to unequal utility across sensitive subgroups. To mitigate this issue, the authors propose a group-specific modeling strategy that constructs separate probabilistic graphical models for each sensitive group within a differentially private synthesis framework. This approach preserves overall data utility while substantially improving utility equity among groups. Empirical evaluations on both synthetic and real-world datasets confirm the presence of such disparate impacts and demonstrate that the proposed method achieves a favorable trade-off between fairness and utility, outperforming baseline approaches in balancing these competing objectives.

Abstract

We revisit the fairness notion of disparate impact for synthetic data generation (SDG), that assesses whether the utility of generated records is the same across sensitive groups. Our approach departs from existing work on fair SDG, that address the problem of correcting for undue biases in the observed distribution, hence redefining SDG as learning a distribution that is not that of the real data. By contrast, non-disparate impact is notably achieved when the synthetic and real distributions are the same. We expose reasons why SDG may fail to reach that solution and discuss why approximation and estimation errors occur and can be disparate across groups. We notably look into the expressive power of SDG methods relative to distribution complexity, sampling errors due to group proportions, and estimation errors induced by differential privacy mechanisms. We illustrate cases of disparate impact on both artificial and real-world data, focusing on SDG methods that rely on probabilistic graphical models. We also introduce a strategy of learning group-wise SDG models and illustrate how it can improve both the overall utility and its parity in many settings.

Problem

• disparate impact
• synthetic data generation
• fairness
• distribution disparity
• sensitive groups

Innovation

• synthetic data generation
• disparate impact
• fairness
• probabilistic graphical models
• differential privacy