Technical
collection and use for AI development
Data activities across the AI model lifecycle.
Definition
Defined activities covering ingestion, cleaning, labeling, training, validation, and deployment of AI models; the Guide treats these lifecycle stages as distinct processing operations with specific source-vetting, pre-ingest filtering, and technical mitigation obligations to manage privacy risks and compliance under PIPA.
Related Terms
Development / Training
Phase when an AI model learns from datasets to be created or tuned....
Development / Training phase
Phase when the system learns from datasets....
AI system learning and development
Activities to collect and process personal data for model training....
Data processing
Any operation performed on personal data....
Processing for training
Collection and retention of personal data to train ML models....