With the advent of generative modeling techniques, synthetic data and its use
has penetrated across various domains from unstructured data such as image,
text to structured dataset modeling healthcare outcome, risk decisioning in
financial domain, and many more. It overcomes various challenges such as
limited training data, class imbalance, restricted access to dataset owing to
privacy issues. To ensure the trained model used for automated decisioning
purposes makes a fair decision there exist prior work to quantify and mitigate
those issues. This study aims to establish a trade-off between bias and
fairness in the models trained using synthetic data. Variants of synthetic data
generation techniques were studied to understand bias amplification including
differentially private generation schemes. Through experiments on a tabular
dataset, we demonstrate there exist a varying levels of bias impact on models
trained using synthetic data. Techniques generating less correlated feature
performs well as evident through fairness metrics with 94\%, 82\%, and 88\%
relative drop in DPD (demographic parity difference), EoD (equality of odds)
and EoP (equality of opportunity) respectively, and 24\% relative improvement
in DRP (demographic parity ratio) with respect to the real dataset. We believe
the outcome of our research study will help data science practitioners
understand the bias in the use of synthetic data.

本研究旨在建立使用合成数据训练的模型在偏见和公平之间的权衡，并研究了合成数据生成技术的变体，包括差分隐私生成方案，通过实验表明，合成数据训练的模型存在不同程度的偏差影响，且生成的特征不相关的技术表现良好。该研究有助于数据科学实践者理解合成数据的使用中的偏见。