AI-Generated Synthetic Data: Opportunities and Challenges

Artificial intelligence has revolutionized data generation by creating synthetic datasets that replicate the properties of real-world data. Generative models such as GANs, VAEs, transformers, and diffusion models enable the production of realistic and diverse data across domains. Synthetic data addresses challenges of scarcity, privacy, and bias while offering scalability and reproducibility. At the same time, it raises concerns about validation, bias reproduction, and ethical use. This presentation highlights the key methods, benefits, challenges, and future potential of AI-generated synthetic data.