Synthetic Data Is a Dangerous Teacher

Synthetic data has been touted as a powerful tool for training machine learning models without compromising privacy or data security. However, the use of synthetic data comes with its own set of risks and challenges that should not be overlooked.

One of the biggest dangers of relying too heavily on synthetic data is that it may not accurately reflect the complexities and nuances of real-world data. Models trained on synthetic data may perform well in controlled environments, but struggle when faced with the unpredictability of real-world scenarios.

Furthermore, synthetic data can introduce biases and inaccuracies that can have far-reaching consequences. If synthetic data is not properly validated and tested, it can lead to erroneous conclusions and decisions that impact individuals and society as a whole.

It is important for organizations and researchers to approach synthetic data with caution and skepticism. While it can be a useful tool for certain applications, it is not a perfect substitute for real-world data and should be used judiciously.