Were the data anonymized?

Data anonymization is the process of removing or modifying personal information in such a way that the individual cannot be identified.

In machine learning projects, anonymization is often used to comply with privacy requirements and legislation (e.g., GDPR). This may include:

• Removing or masking identifiers (names, phone numbers, addresses).
• Generalizing data (e.g., replacing exact age with age groups).
• Applying differential privacy techniques.

If data has been anonymized, this is usually documented, and special techniques are used to preserve the usefulness of the data for model training while protecting personal information.