A field guide to AI positions

Synthetic data

Using AI.

Dated sources are over 18 months old; other dates are unknown.

Publication dates and source age

Sources counted: 3

Newest dated source: 2024-11-20

Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.

Some publication dates are unknown; the newest dated source may not be the newest source overall.

Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.

Open in the glossary Reading notes · Structured record

Prepared with AI assistance. Not independently fact-checked. Original sources and qualifications are below.

In plain language

Artificially generated examples used as data. They can support testing and analysis, but being synthetic does not guarantee privacy. [1] [2]

Reference this explanation or suggest a correction

Explanation based on the linked sources. Claim ID: claim-term-synthetic-data-1a5192ba3d7825ca26b24a72. Edition 0.20.0.

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Limits & distinctions

Synthetic content describes generated media; synthetic data emphasizes its use as examples for analysis, testing or training. Some generation and evaluation methods can expose information about original records, so privacy needs separate assessment. [1] [2] [3]

Reference this explanation or suggest a correction

Explanation based on the linked sources. Claim ID: claim-term-synthetic-data-09422ce4d5c74cb753a9bb98. Edition 0.20.0.

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A fuller explanation

A generator can produce new rows that resemble patterns in an original table. The resulting dataset can be compared with real data to check whether useful patterns were preserved. For example, a team could generate sample customer records to test software. [1]

Reference this explanation or suggest a correction

Explanation based on the linked sources. Claim ID: claim-term-synthetic-data-1e4c26398ee834b2e16dc7b8. Edition 0.20.0.

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How it relates to the map

Map context: this is a data-production method. We do not place it on the pace or concern axes. [1]

Reference this explanation or suggest a correction

Editorial interpretation or qualification. Claim ID: claim-term-synthetic-data-25f52a21ad9f2e54a62ed1b2. Edition 0.20.0.

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https://theaiatlas.org/ideas/synthetic-data/

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Sources and what we read

  1. 1. Welcome to the SDV!

    DataCebo / Synthetic Data Vault · First-hand source · Published date unavailable · Material last read 2026-09-15

    Publication dates and source age

    Sources counted: 1

    Publication dates are unavailable.

    Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.

    Some publication dates are unknown; the newest dated source may not be the newest source overall.

    Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.

    Read scope is described in the source note.

    No archive check recorded.

    Read the library overview and its generation and evaluation workflow for tabular synthetic data. This is developer documentation; no commercial claim of quality or privacy is treated as independently verified.

    Source ID: glossary-wide-synthetic-data

  2. 2. The Inadequacy of Similarity-based Privacy Metrics: Privacy Attacks against "Truly Anonymous" Synthetic Datasets

    Georgi Ganev and Emiliano De Cristofaro / arXiv · First-hand source · Published 2023-12-08 · Updated 2025-05-07 · Material last read 2026-09-15

    Source is over 18 months old.

    Publication dates and source age

    Sources counted: 1

    Newest dated source: 2023-12-08

    Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.

    Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.

    Read scope is described in the source note.

    No archive check recorded.

    Read the abstract and version history. The reported attacks show that passing the studied similarity-based tests does not establish anonymity. The entry does not claim that every synthetic dataset leaks personal information.

    Source ID: glossary-wide-synthetic-privacy

  3. 3. Reducing Risks Posed by Synthetic Content: An Overview of Technical Approaches to Digital Content Transparency

    National Institute of Standards and Technology · First-hand source · Published 2024-11-20 · Material last read 2026-09-15

    Source is over 18 months old.

    Publication dates and source age

    Sources counted: 1

    Newest dated source: 2024-11-20

    Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.

    Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.

    Read scope is described in the source note.

    No archive check recorded.

    Read the introduction, selected provenance/detection passages and Appendix D's audio/video examples. Date checked on NIST's landing page. No detector certified here.

    Source ID: nist-synthetic-content

Edition and machine-readable evidence

Content version 0.20.0. Evidence cutoff 2026-09-15; this does not mean every source was read on that day.

Pinned complete dataset · Complete evidence page · Agent consumption guide