Synthetic Data Is a Dangerous Teacher

When it comes to learning and teaching, data is often seen as a valuable resource. It can provide insights, help track progress, and guide decision-making. However, not all data is created equal.

Synthetic data, which is generated by algorithms rather than being collected from real-world observations, can be a dangerous teacher. While it may seem like a shortcut to obtaining large amounts of data quickly, synthetic data lacks the authenticity and complexity of real-world data.

Using synthetic data to train models or make decisions can lead to biased or inaccurate results. Without the nuances and unpredictability of real-world data, synthetic data can reinforce existing biases or create unrealistic expectations.

It’s important to remember that while synthetic data may seem like a convenient solution, it is no substitute for the real thing. If we want our models and systems to learn and grow in a way that truly reflects the world we live in, we must rely on authentic, diverse, and unbiased data.