Navigating the Challenges of Synthetic Data in AI

This chapter explores the complexities of using synthetic data to train AI models, discussing potential pitfalls such as model collapse and error amplification. It emphasizes the necessity of strict oversight and verification processes to ensure the integrity and reliability of AI systems, especially in sensitive fields like healthcare. The conversation also highlights the fragility of classifiers and the risks inherent in AI decision-making, calling for careful balance between synthetic and real data.