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.

Interpretability: mentions per chapter11 mentions of the term across 4 of the 23 chapters of the Body of Knowledge; the chapters the glossary cross-references are drawn solid.Cross-referenced chapterOther chapter10 Reading List: 1 mention10 Reading List111 AI Defined: 4 mentions11 AI Defined414 Development: 2 mentions14 Development216 Fairness & XAI: 4 mentions16 Fairness & XAI4No mentions in the other 19 chaptersSource: the Body of Knowledgev0.5.0
Interpretability: mentions per chapter11 mentions of the term across 4 of the 23 chapters of the Body of Knowledge; the chapters the glossary cross-references are drawn solid.Cross-referenced chapterOther chapter00010203040506070810 Reading List: 1 mention11011 AI Defined: 4 mentions411121314 Development: 2 mentions2141516 Fairness & XAI: 4 mentions41617181920212223Source: the Body of Knowledge v0.5.0
Mentions per chapter How often each chapter names the term, with the chapters the glossary cross-references drawn solid. The longest bar is where to read the term in use; bars spread across many chapters mark a term that runs through the discipline.
Mentions as a table
Mentions of Interpretability per chapter
ChapterMentionsCross-referenced
00 Preface0No
01 Definition0No
02 Why Now0No
03 Values & Principles0No
04 The Stack0No
05 Patterns0No
06 The Role0No
07 Maturity Model0No
08 Regulatory Map0No
10 Reading List1No
11 AI Defined4No
12 Governance Program0No
13 Risk Management0No
14 Development2No
15 Deployment0No
16 Fairness & XAI4Yes
17 Incidents0No
18 EU AI Act0No
19 Privacy & AI0No
20 Existing Law0No
21 AI Laws Worldwide0No
22 Principles & Standards0No
23 AI Agents0No
  • 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}
}