Synthetic Data
Artificially generated data, rather than real-world measurements, created through AI algorithms or statistical models that retains the statistical characteristics and patterns of the original data to be used for analysis and model training.
Detailed explanation
Why It Matters in Tool Selection
It plays a crucial role in early-stage model development where acquiring data is challenging, or in enterprise environments where privacy protection is the top priority. When selecting a synthetic data generation tool, you must look beyond the mere ability to generate data and check whether it features high fidelity (how well the generated data maintains the statistical properties of the original) and security technologies (such as Differential Privacy) that prevent the source data from being reverse-engineered.
What to Check
- Fidelity: How accurately does the synthetic data maintain the statistical correlations of the real data?
- Utility: Does the model trained on synthetic data perform well in real-world environments?
- Privacy: Does the application of Differential Privacy prevent re-identification of the original data?
- Bias Mitigation: Can it remove biases contained in the source data or reinforce specific datasets?
Example
For training autonomous driving AI, virtual images of extreme weather conditions (heavy snow, rainstorms) or scenarios of pedestrians suddenly appearing at night—which are difficult to capture on real roads—can be generated for training. Additionally, it is used to train predictive models for drug discovery by generating clinical data for tens of thousands of virtual patients without exposing the personal information of actual cancer patients.