Publikation
Towards Fairness in Synthetic Healthcare Data: A Framework for the Evaluation of Synthetization Algorithms
Yannik Warnecke; Martin Kuhn; Felix Diederichs; Tobias J Brix; Lena Clever; Ralph Bergmann; Dominik Heider; Michael Storck
In: Studies in Health Technology and Informatics, Vol. 331, Pages 25-34, IOS Press, Netherlands, 2025.
Zusammenfassung
Synthetic data generation is a rapidly evolving field, with significant potential for improving data privacy. However, evaluating the performance of synthetic data generation methods, especially the tradeoff between fairness and utility of the generated data, remains a challenge. In this work, we present our comprehensive framework, which evaluates fair synthetic data generation methods, benchmarking them against state-of-the-art synthesizers. The proposed framework consists of selection, evaluation, and application components that assess fairness, utility, and resemblance in real-world scenarios. The framework was applied to state-of-the-art data synthesizers, including TabFairGAN, DECAF, TVAE, and CTGAN, using a publicly available medical dataset. The results reveal the strengths and limitations of each synthesizer, including their bias mitigation strategies and trade-offs between fairness and utility, thereby showing the framework's effectiveness.