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Synthetic Data

Dati sintetici

Synthetic data is artificially generated data, often by other AI models, rather than collected from the real world. It reproduces the statistical properties of real data without containing its records: a synthetic medical-records dataset "resembles" the real one but describes no existing patient.

Definition

Main uses: training models when real data is scarce, costly or sensitive (healthcare, finance), testing systems without exposing personal data (a concrete route to privacy by design), balancing unbalanced datasets, and generating examples for fine-tuning. AI labs use synthetic data heavily to train their most recent models.

Risks worth knowing: low-quality synthetic data passes on the generator's flaws, biases can be inherited or amplified, and recursive training on other models' output can degrade quality (the phenomenon called "model collapse"). Validation against real data remains indispensable.

Related terms

• Training Data
• Privacy by Design
• Fine-tuning
• AI Bias
• Machine Learning

More in AI Fundamentals

• LLM (Large Language Model)
• Token
• Context Window
• Temperature
• AI Hallucination
• Fine-tuning
• RAG (Retrieval Augmented Generation)
• Embedding

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