Synthetic Data

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

Information that can identify an individual (directly or indirectly); requires careful handling and compliance.

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.

Measure of consistency across labelers; low agreement indicates ambiguous tasks or poor guidelines.

Selecting the most informative samples to label (e.g., uncertainty sampling) to reduce labeling cost.

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

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