- Overfitting: With an abundance of synthetic data, models may become overly specialized in the synthetic data and fail to generalize to new, unseen data.
- Lack of diversity: Synthetic data may not capture the diversity of real-world data, which can lead to models that are not robust or adaptable to different scenarios.
- Dependence on data generation: If AI models rely too heavily on synthetic data, they may become dependent on the quality and accuracy of the generated data, which can be a single point of failure.
- Difficulty in debugging: With synthetic data, it can be challenging to identify and debug errors, as the data may not accurately reflect real-world scenarios.
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