PII

Processes and controls for data quality, access, lineage, retention, and compliance across the AI lifecycle.

Standardized documentation describing intended use, performance, limitations, data, and ethical considerations.

Structured dataset documentation covering collection, composition, recommended uses, biases, and maintenance.

Central system to store model versions, metadata, approvals, and deployment state.

Ordering training samples from easier to harder to improve convergence or generalization.

Expanding training data via transformations (flips, noise, paraphrases) to improve robustness.

Artificially created data used to train/test models; helpful for privacy and coverage, risky if unrealistic.

Training across many devices/silos without centralizing raw data; aggregates updates, not data.