In today’s era of data-driven technology, there is a growing interest in synthetic data. This refers to artificially generated data that resembles real-world data but does not contain any personal information. The rise in popularity of synthetic data is driven by its potential to protect privacy, enhance data availability for research, and reduce bias in machine learning models. This study examines the policies governing the creation, usage, and distribution of synthetic data. While synthetic data can be a valuable tool in safeguarding individuals’ privacy, it also poses challenges, such as ensuring its accuracy and authenticity. An effective synthetic data policy should find a balance between privacy concerns and the usefulness of data, ensuring that it can be utilized effectively while upholding ethical and legal standards. To fully benefit from synthetic data and overcome its inherent difficulties, organizations and institutions need to establish standardized guidelines and adopt best practices.
Exploring the Potential and Challenges of Synthetic Data in AI Model Training for Sustainable Development: A Study (arXiv:2309.00652v1 [cs.LG])
