Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Data
This perspective addresses the broader opportunities and challenges of synthetic data for the ML community, but it is incremental as it synthesizes existing ideas without introducing new methods or results.
The paper explores the potential of synthetic data to become a dominant force in machine learning, extending beyond privacy to applications like fairness and data augmentation, while highlighting the key challenge of quantifying trust in findings derived from synthetic data.
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.