Disjoint Generation of Synthetic Data
Two research papers explore novel approaches to synthetic data generation (SDG) with a focus on fairness and privacy. The first paper revisits the concept of disparate impact in SDG, examining how approximation and estimation errors can disproportionately affect different groups and proposing group-wise SDG models to improve utility and parity. The second paper introduces a framework for disjoint generative models, partitioning datasets for separate generation and then combining them without common identifiers, which enhances privacy and computational feasibility while maintaining utility. AI
IMPACT These papers introduce new methodologies for synthetic data generation that could improve fairness and privacy in AI models trained on generated data.