Ethical Synthetic Data Generation via Fairness-Aware Generative Models

Abstract

Synthetic data has emerged as a crucial component in AI model training, offering privacy protection and enhanced data diversity. However, generative models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) often inherit and amplify biases present in training datasets, leading to ethical concerns. This paper explores fairness-aware generative models that embed fairness constraints (e.g., demographic parity, equalized odds) to mitigate bias during data synthesis. We review methods for bias quantification in synthetic data, regulatory compliance frameworks, and algorithmic advancements in fair synthetic data generation. The research also presents an evaluation framework for fairness, utility, and privacy trade-offs, followed by a discussion on future research directions.