To mitigate these risks, it's essential to ensure that synthetic data is:
- High-quality and accurate
- Representative of real-world scenarios
- Diverse and inclusive
- Transparent and explainable
- Reviewed and validated by humans
- Used in conjunction with real-world data
- Regularly updated and refined
By being aware of these potential risks and taking steps to mitigate them, we can ensure that AI models are developed and deployed responsibly and effectively.