Synthetic Data Is a Dangerous Teacher

Synthetic data, or artificially generated data, is becoming increasingly popular in the field of machine learning and artificial intelligence. While it may seem like a convenient way to train models without collecting real-world data, synthetic data can be a dangerous teacher.

One of the biggest dangers of synthetic data is that it may not accurately represent the real-world scenarios that your model will encounter. This can lead to overfitting and poor generalization, as the model is not trained on the diverse and complex patterns present in actual data.

Furthermore, using synthetic data can also lead to biased or skewed models. Since synthetic data is generated by algorithms, it may inherit the biases present in the underlying data or the generation process itself. This can result in models that perpetuate discrimination and unfair decision-making.

It is important for researchers and practitioners to be cautious when using synthetic data in their models. They should carefully evaluate the quality and representativeness of the synthetic data to ensure that the models are robust and fair.