Synthetic Data

Synthetic data is data generated artificially rather than collected from real-world events. AI models themselves are increasingly used to generate synthetic training data for other AI models, creating a feedback loop.

Use cases for synthetic data:

• Privacy Protection: Generating data that doesn’t contain real personal information
• Data Scarcity: Creating additional training examples when real data is limited
• Edge Cases: Generating rare scenarios for robust model training
• Cost Reduction: Cheaper than collecting and labeling real data
• Bias Mitigation: Creating balanced datasets to reduce model bias

Concerns about synthetic data include quality degradation (model collapse when trained recursively on synthetic data), potential for amplifying biases, and the lack of real-world grounding. Despite these challenges, synthetic data is widely used in autonomous vehicle training, healthcare AI, and NLP.