Generating synthetic data through generative models is gaining interest in the ML community and beyond. In the past, synthetic data was often regarded as a means to private data release, but a surge of recent papers explore how its potential reaches much further than this -- from creating more fair data to data augmentation, and from simulation to text generated by ChatGPT. In this perspective we explore whether, and how, synthetic data may become a dominant force in the machine learning world, promising a future where datasets can be tailored to individual needs. Just as importantly, we discuss which fundamental challenges the community needs to overcome for wider relevance and application of synthetic data -- the most important of which is quantifying how much we can trust any finding or prediction drawn from synthetic data.
@article{arxiv.2304.03722,
title = {Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Data},
author = {Boris van Breugel and Mihaela van der Schaar},
journal= {arXiv preprint arXiv:2304.03722},
year = {2023}
}