AI Governance Engineer
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Interpretability

By Updated

In NIST's framing, the meaning of a system's output in the context of its purpose: why a decision was made and what it means to the user 1. An inherently interpretable model, such as a scorecard or a shallow tree, is its own explanation.

Developed in
ch. 16, Transparency, interpretability and explainability
ch. 16, Interpretable by design or explained after the fact
Chapters
ch. 16, Fairness & XAI
Contrast with
Explainability
Source
1 numbered reference, listed below

Where it is used

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

  • Transparency

    In NIST's framing, how far information about an AI system and its outputs reaches the people who interact with it: what happened.

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). Interpretability. In AI Governance Engineering: The Thesis & Body of Knowledge (v0.5.0), Glossary. https://doi.org/10.5281/zenodo.22956197. https://aigovernanceengineer.com/glossary/interpretability. CC BY 4.0

BibTeX

@misc{aige2026interpretability,
  author  = {Jorge García Aibar},
  title   = {{Interpretability}},
  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/interpretability}
}