AI Governance Engineer
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Transparency

By Updated

In NIST's framing, how far information about an AI system and its outputs reaches the people who interact with it: what happened 1. Evidenced by records of what ran (registry, model and system cards, logs) and by the disclosures the law requires.

Developed in
ch. 16, Transparency, interpretability and explainability
ch. 11, Contrast pairs
Chapters
ch. 11, AI Defined · ch. 16, Fairness & XAI
Contrast with
Explainability · Interpretability
Source
1 numbered reference, listed below

Commonly confused

  • Transparency versus Explainability

    Transparency
    In NIST's framing, how far information about an AI system and its outputs reaches the people who interact with it: what happened.
    Explainability
    In NIST's framing, a representation of the mechanisms behind a system's operation: how a decision was made.

    The differenceWhat happened, from records of what ran, against how one decision was made.

    Why it mattersTwo artefacts: registry, cards and logs for the first; an explanation method with a fidelity test for the second.

Where it is used

20 chapters of the Body of Knowledge use the term. Each link opens the first section that does.

Patterns that use this term

16 pattern pages use the term; the 10 that use it most:

  • Concept drift

    A change in the relationship between a system's inputs and the correct output, so the same input should now get a different answer.

  • Data drift

    A change in the distribution of the inputs a system sees in production relative to the data it was validated on, such as a new customer segment or a changed…

  • Opacity

    The inability of a person to follow how a system reached an output.

  • Content provenance (C2PA)

    Signed, tamper-evident information about where a piece of content came from and how it was edited, bound to the asset.

  • Algorithmic management

    The use of automated monitoring and decision systems to direct, evaluate or sanction workers.

  • GPAI Code of Practice

    The voluntary instrument (published 10 July 2025) that general-purpose-AI providers use to demonstrate compliance with their AI Act obligations until…

Sources

  1. [1] Artificial Intelligence Risk Management Framework (AI RMF 1.0), NIST AI 100-1 (risk tolerance and residual risk; seven trustworthy characteristics; transparency answers "what happened", explainability "how", interpretability "why"; MAP 1.1 intended purposes; MANAGE 1.1 go/no-go determination; profiles). NIST. 2023-01-26. https://doi.org/10.6028/NIST.AI.100-1 (verified: primary)

Definitions of legal terms paraphrase the cited text, which governs. Dated statements are as of .

Cite this term

García Aibar, J. (2026). Transparency. In AI Governance Engineering: The Thesis & Body of Knowledge (v0.5.0), Glossary. https://doi.org/10.5281/zenodo.22956197. https://aigovernanceengineer.com/glossary/transparency. CC BY 4.0

BibTeX

@misc{aige2026transparency,
  author  = {Jorge García Aibar},
  title   = {{Transparency}},
  note    = {Glossary, AI Governance Engineering: The Thesis \& Body of Knowledge, version 0.5.0},
  year    = {2026},
  doi     = {10.5281/zenodo.22956197},
  url     = {https://aigovernanceengineer.com/glossary/transparency}
}