- Limited transfer learning: Synthetic data may not be transferable to other domains or tasks, which can limit the applicability of AI models.
- Risk of over-optimization: Synthetic data can be optimized for specific tasks or scenarios, which can lead to models that are over-optimized for a particular use case and may not generalize well to other situations.
- Lack of human oversight: Synthetic data may not be reviewed or validated by humans, which can lead to errors or inaccuracies in the data.
- Potential for misuse: Synthetic data can be used to manipulate or deceive AI models, which can have serious consequences in areas like finance, healthcare, or national security.
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