Synthetic data can be beneficial for researchers and analysts aiming to work with real-world data while protecting privacy.

Fidelity plays a crucial role in determining the quality of synthetic data on various tasks, and it is directly compared to the real data.

Understanding the concept of fidelity will help you make better choices when working with synthetic datasets.

Key Consideration 1: Separating Fidelity from Utility

• Fidelity is distinctly defined as the measures directly comparing synthetic and real datasets, while

• utility focuses on indirect comparisons through models or task performance.

• Recognizing this difference ensures a better understanding of synthetic data quality.

Key Consideration 2: Understanding High-Level Fidelity

• Fidelity assesses how well synthetic data statistically match real data.

• Achieving full statistical similarity might be challenging due to privacy constraints or biases present in the data.

• Being mindful of these limitations will help you manage your expectations when analyzing synthetic data.

Key Consideration 3: Choosing the Right Fidelity Metrics

• Instead of aiming for "full" statistical match, consider examining low-dimensional marginals, syntactical accuracy, or inspecting feature distribution conditional on biased features.

• Selecting the most appropriate metric for your task will aid in more accurate assessments of synthetic data quality.

Are you working with synthetic data? Join the conversation and discover the nuances of assessing fidelity in synthetic datasets!