Synthetic Data Is a Dangerous Teacher

Synthetic data, also known as artificial data or simulated data, is data that is generated by computer programs rather than collected from actual sources. While synthetic data can be useful in certain scenarios, it can also be a dangerous teacher in the wrong hands.

One of the main dangers of relying on synthetic data is that it may not accurately reflect the real world. This can lead to biases and inaccuracies in data analysis and decision-making processes. When synthetic data is used to train machine learning models, for example, it may not capture the complexity and nuance of real-world data, leading to models that perform poorly in practice.

Furthermore, synthetic data can also perpetuate existing biases and inequalities. If the synthetic data is generated based on biased or incomplete real data, it will only serve to reinforce those biases in the resulting models and analyses. This can have serious consequences in fields such as healthcare, finance, and criminal justice, where biased data can lead to discriminatory outcomes.

It is important for data scientists and analysts to be aware of the limitations of synthetic data and to use it thoughtfully and responsibly. Synthetic data should be validated against real-world data whenever possible, and its limitations should be clearly communicated to stakeholders. Only by approaching synthetic data with caution and skepticism can we avoid the dangers of its misuse.