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name: training-data-annotation description: >- Design and improve human annotation programs for machine-learning training, validation, and evaluation data, including task and label schemas, active acquisition, annotator guidance, calibration, disagreement, adjudication, provenance, and cost-quality stopping. Do not use for general statistical inference, data storage pipelines, model training, or annotation-interface implementation; route those concerns to data-scientist, data-engineering, ml-engineering, or product-design-and-ux. license: MIT compatibility: Reference-only methodology; no runtime dependency. metadata: spec-version: '1.0' tags: annotation, active-learning, labeling, human-review, training-data

Training Data Annotation

Treat annotation as a measurement system and a human workflow. The objective is useful, auditable labels for a defined downstream decision, not the largest possible label count.

Workflow

1. Define the downstream task, population, label unit, label set, edge cases, abstention label, and what constitutes an unusable item. Separate training, validation, held-out evaluation, and challenge data before sampling.
2. Write an annotation guide with positive and negative examples, boundary rules, source requirements, escalation, and a change log. Have domain owners approve consequential definitions.
3. Establish provenance and rights: source, collection time, license/consent, transformations, sensitive fields, access, retention/deletion, and dataset version. Do not move data into a labeling system until this record is clear.
4. Acquire items using a declared mix of representative sampling and targeted acquisition. Uncertainty finds confusing items; diversity and novelty find gaps; stratification protects rare or high-risk slices. Keep acquisition batches identifiable and retain a representative held-out audit sample.
5. Qualify annotators for the task, train them on calibration items, and measure quality and workload. Record role and relevant expertise in restricted metadata. Do not use agreement as a universal truth score.
6. Run blinded calibration and periodic blind rechecks. Compare labels with adjudicated references where available; preserve meaningful disagreement, ambiguity, and abstention instead of forcing consensus.
7. Review disagreement by item, label, slice, annotator, and guide version. Adjudicate only with a stated rule and qualified authority. Feed confirmed guide defects back into training and re-review affected labels.
8. Stop or continue based on marginal downstream value, coverage, quality, cost, and time. An item quota may stop spending but cannot by itself establish sufficient quality. Freeze the manifest and hand off to data-engineering for storage/orchestration and ml-engineering for training use.

Acquisition guardrails

Never let model confidence define the whole dataset. Sample a representative held-out audit independently of active-learning selection, and compare active batch outcomes against it. Track uncertainty method, model/version, seed, selection scores, slice quotas, and excluded items. If confidence is uncalibrated or the model is out of distribution, treat its ranking as a search heuristic and increase diversity, novelty, and human review.

Human and quality guardrails

Design pay, volume, time limits, breaks, and escalation so workers can perform the task reliably; route employment, compensation, and legal questions to the appropriate human owner. Blind annotators to model predictions when measuring independent labels. If preannotations are shown, measure anchoring by a blinded control or separate review pass. Report agreement by label type and slice with the uncertainty and prevalence needed to interpret it. A minority label can be correct when the item is ambiguous; adjudication must explain the rule used.

Routing and exit

Load references/task-and-label-design.md for schemas and guide design, references/acquisition-and-stopping.md for sampling and stopping, references/annotator-quality.md for people and disagreement, and references/provenance-and-rights.md for dataset authority. Use the templates when a durable record is needed. For primary methodology sources and transfer limits, read references/source-index.md.

Route statistical estimators and power to data-scientist, storage and pipelines to data-engineering, model training and leakage checks to ml-engineering, and labeling-interface interaction design to product-design-and-ux.

When not to use

Do not use this skill as the primary owner for statistical inference or experimental design, data platforms, model training, or UI implementation. Those neighboring skills consume this skill's annotation manifest and quality evidence.

Templates

• annotation guide — use when recording this stage of the annotation workflow.
• annotation plan — use when recording this stage of the annotation workflow.
• quality report — use when recording this stage of the annotation workflow.