Data Preparation, Data Quality and Safe Data Use for Artificial Intelligence
Addresses the safe preparation of data for AI applications in line with quality criteria, standardization, authorized use and anonymization principles.
Training content
- Data quality metrics
- Cleaning–standardization
- Data dictionary
- Permitted data use
- Anonymization/pseudonymization and data traceability
At the end of this training, participants
- Will be able to evaluate the impact of data quality on performance in AI applications using concrete criteria.
- Will be able to produce datasets suitable for model or generative AI use by standardizing data preparation steps.
- Will be able to recognize the limits of internal data sharing and apply authorization and security principles.
- Will be able to develop a data processing approach that preserves analytical value using methods compatible with personal data protection.
- Will be able to report which output is based on which data by ensuring data traceability.
In this way, a reliable, auditable and compliant AI data foundation will be established.