What is synthetic data in the context of AI?

Synthetic data refers to data that is artificially generated rather than obtained from real-world events or interactions. In the context of AI, it often serves as a valuable resource for training machine learning models while preserving the privacy and confidentiality of sensitive information. Since synthetic data can be created to resemble the statistical properties of real datasets without exposing actual user data, it allows developers and researchers to design and test algorithms without risking privacy breaches.

This aspect of synthetic data is particularly important in industries like healthcare or finance, where real data may contain personally identifiable information or other sensitive elements. By using synthetic data, organizations can still derive insights and construct robust AI models while adhering to data protection regulations.

In contrast, the other options don't accurately define synthetic data within the AI context. The first option suggests that synthetic data is a scaled-down version of real data, which does not capture the essence of synthetic data's purpose. The third option describes data collected from user interactions, which refers to real data rather than synthetic. The fourth option points to publicly available data meant for research, which again does not address the synthetic nature or privacy-related aspect of synthetic data.