Synthetic Data Is a Dangerous Teacher

Synthetic data, or artificially generated data that mimics real-world data, has gained popularity in recent years due to its potential to address privacy concerns and data scarcity issues. However, relying solely on synthetic data for training machine learning models can be a dangerous practice.

One of the main pitfalls of synthetic data is that it may not accurately capture the complexity and nuances of real-world data. This can lead to models that perform poorly when applied to real-world scenarios, as they have not been trained on truly representative data.

Furthermore, synthetic data can introduce biases and inaccuracies that are not present in real data, leading to erroneous conclusions and decisions based on the model’s predictions.

While synthetic data can be a useful tool for augmenting real data and addressing certain challenges in machine learning, it should not be used as a substitute for real data. It is important to validate and test models trained on synthetic data using real data to ensure their reliability and accuracy.