What are the limitations of synthetic data?

What are the limitations of synthetic data?

Synthetic data can lack the complexity and variability of real-world data, leading to potential biases.

Can synthetic data be used for training AI models effectively?

Yes, but it may not capture all nuances, which can affect model performance in real-world scenarios.

What are some common biases found in synthetic data?

Common biases include demographic imbalances and unrealistic feature distributions that don't reflect reality.

How can we mitigate the limitations of synthetic data?

Mitigation strategies include combining synthetic data with real data and continuously validating models against real-world outcomes.

Are there industries where synthetic data is particularly useful?

Yes, industries like healthcare and finance use synthetic data for privacy-preserving analysis and model training.