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Annotation quality guide

Synthetic Data Lineage and Limitations Review: Record What Was Generated and What Was Not Proven

Trace source, generation activity, transformations, evaluation slices, and unresolved limitations without treating synthetic data as automatically private or representative.

Synthetic data is an additional artifact with its own lineage, not a shortcut around source evidence. Review which source material, configuration, generation activity, filters, and evaluation slices produced a release. Keep utility, coverage, privacy-related evidence, and residual risk separate. This guide is a static worksheet, not a generator, privacy guarantee, bias certificate, or legal opinion.

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Define the synthetic artifact and its source chain

The source dates shown in this guide identify documentation checks on 22 October 2026, not original publication dates. Follow each citation for the risk and ethics vocabulary being discussed. Begin with a controlled release id, format, schema or ontology version, source artifact references, generation activity, configuration reference, filter stages, and evaluator. Do not place private source rows, customer payloads, secrets, or sensitive examples in a public synthetic fixture.

Define the synthetic artifact and its source chain (table)
Lineage itemSynthetic recordUnknown if absent
Source setsource-release-03 referenceWhich material influenced generation?
Generation activityactivity-synth-11Which process created the release?
Configurationconfig-ref-11, content withheldWhich settings and model version?
Transform stagesfilter, dedupe, mapWhat changed after generation?
Output releasesynthetic-release-04Which artifact was evaluated?
Agent or rolesynthetic owner roleWho supplied or approved the record?
Intended useevaluation fixture onlyWhich use boundary applies?

A lineage record should distinguish source use from source copying. Record the relationship and evidence location without claiming that the output cannot reveal source information. A synthetic label is not proof that the source was removed, and a new identifier is not proof that a person or organization cannot be inferred.

Measure coverage as a set of declared checks

Do not summarize a synthetic release as representative without defining the population, slices, comparison variables, and evaluation method. Record which checks were run and which were unavailable. A release can resemble an aggregate while missing rare, boundary, multilingual, temporal, or minority patterns. The unknown state belongs in the result.

Synthetic coverage worksheet

Measure coverage as a set of declared checks code example, JSON
{
  "release_id": "synthetic-release-04",
  "source_reference": "source-release-03",
  "slices": ["common-case", "edge-case", "time-window-2"],
  "checks": [
    {"name": "schema_shape", "status": "observed"},
    {"name": "rare_slice", "status": "unavailable"},
    {"name": "source_reconstruction_probe", "status": "not_run"}
  ],
  "limitations": ["coverage for rare slices is unknown", "privacy-related evidence is method-specific"],
  "disposition": "conditional"
}
Measure coverage as a set of declared checks (table)
DimensionEvidence to retainSafe wording
SchemaFields, types, constraints, versionStructure was checked for the named release
DistributionDeclared variables and comparison windowObserved comparison is scoped
SubgroupNamed slice and evidence statusThis slice is observed or unknown
TemporalSource and generated time windowsTime coverage was or was not checked
UtilityTask-specific evaluation recordResult applies only to this evaluation
Disclosure riskMethod, probe, and unresolved gapsNo automatic privacy conclusion

Write limitations that survive a favorable fixture

NIST guidance is a reminder to connect claims to a method and threat model. A synthetic release can carry generation uncertainty, inherited source bias, missing subpopulations, memorized patterns, or new artifacts introduced by filtering. Record the evidence that supports each observation and avoid turning one pass into a universal claim.

Write limitations that survive a favorable fixture (table)
Observed factTempting overclaimQualified disposition
No exact duplicate in a sampleThe output contains no source materialReport sample scope and unknown remainder
Schema matches sourceThe output is representativeKeep schema and coverage checks separate
Common slice resembles sourceAll groups are preservedName unchecked slices
No probe was runThe data is privatePrivacy-related evidence unavailable
A utility test passedThe data is fit for every useName task, slice, and release
A filter removed recordsRisk was eliminatedRecord filter scope and residual unknowns

The UK Government, (opens in a new tab) guidance supports considering intended use and mitigations. Use that as a prompt for an owner-specific review, not as a replacement for a privacy, security, or legal process. If the source population or generation settings are unavailable, say so plainly.

Use a lineage and limitation decision table

A useful review ends with a disposition and a next evidence request. Keep the generated artifact, lineage chain, evaluation observations, and owner decision as separate records. The table uses invented identifiers and avoids raw source examples.

Use a lineage and limitation decision table (table)
CheckEvidence stateNext action
Source referencelinked / partial / unavailableRequest controlled source reference
Generation activityversioned / descriptive only / unknownRecord workflow and configuration
Schema and ontologymatched / changed / unknownVersion the mapping and hold ambiguity
Coverage slicesobserved / unobserved / unavailableName required evaluation slices
Utility evidencetask-specific / not runLimit wording to the checked task
Privacy-related evidencemethod-specific / unavailableEscalate to the appropriate owner process
Residual limitationslisted / incompleteKeep release conditional until reviewed

Keep synthetic-data findings scoped and qualified

This review can document lineage, declared generation stages, checked slices, unavailable evidence, and residual limitations. It cannot establish that data is private, unbiased, representative, accurate, lawful, authorized, or suitable for every downstream use. Unknown evidence remains unknown until the owner supplies a bounded check.

Pair this guide with the data provenance log for artifact history and annotation label ontology versioning for generated labels and definitions. After supported non-health scope, authority to describe the collection, and a controlled lineage packet are established, the qualified appraisal request path may be a next preparation step. It does not generate data, certify privacy, create a quote, match a buyer, issue a legal opinion, or grant a license.

Takeaways

  • Record source, generation activity, configuration, transformations, output identity, agent role, and intended use.
  • Report coverage and utility by named slices and tasks, with unavailable checks visible.
  • Do not treat synthetic labels, new identifiers, or one favorable fixture as a privacy guarantee.
  • Keep inherited bias, generation uncertainty, and residual limitations in the disposition record.
  • Separate lineage evidence from privacy, legal, ownership, and downstream-use decisions.

Sources

  1. National Institute of Standards and Technology: De-Identifying Government Datasets: Techniques and Governance (opens in a new tab)
  2. UK Government: Ethical considerations relating to the creation and use of synthetic data (opens in a new tab)

Use the appraisal path after the synthetic-data packet is organized.

It does not generate data, certify privacy, create a quote, match a buyer, or grant a license.