Fair Synthetic

Fair synthetic data generation aims to create artificial datasets that accurately reflect the original data's statistical properties while mitigating biases against specific demographic groups. Current research focuses on developing generative models, including GANs and diffusion models, often incorporating techniques like knowledge distillation and counterfactual fairness to achieve this goal. These methods are evaluated using comprehensive benchmarking tools that assess fairness, utility, and the quality of the synthetic data, ultimately contributing to more equitable and reliable machine learning applications across various domains. The impact extends to responsible AI development, enabling fairer algorithms and mitigating the risk of discriminatory outcomes.