AI Governance Engineer
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Opacity

By Updated

The inability of a person to follow how a system reached an output. It has three sources (secrecy, technical illiteracy, and the nature and scale of machine learning) 1, each with a different fix: disclosure, literacy, and explanation methods plus behavioural evals.

Developed in
ch. 11, Eight characteristics that break classic IT governance
ch. 11, Contrast pairs
Chapters
ch. 11, AI Defined
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.

Opacity: mentions per chapter15 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: 2 mentions10 Reading List211 AI Defined: 9 mentions11 AI Defined913 Risk Management: 3 mentions13 Risk Management316 Fairness & XAI: 1 mention16 Fairness & XAI1No mentions in the other 19 chaptersSource: the Body of Knowledgev0.5.0
Opacity: mentions per chapter15 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: 2 mentions21011 AI Defined: 9 mentions9111213 Risk Management: 3 mentions313141516 Fairness & XAI: 1 mention11617181920212223Source: 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 Opacity 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 List2No
11 AI Defined9Yes
12 Governance Program0No
13 Risk Management3No
14 Development0No
15 Deployment0No
16 Fairness & XAI1No
17 Incidents0No
18 EU AI Act0No
19 Privacy & AI0No
20 Existing Law0No
21 AI Laws Worldwide0No
22 Principles & Standards0No
23 AI Agents0No
Opacity: related terms and patternsThe term at the centre; related terms on solid edges, the terms it is contrasted with on dashed edges, and the patterns that use it most in the colour of their layer.RelatedContrastOpacityRelated terms (7)Catastrophic forgetting · RelatedCatastrophic forgettingConcept drift · RelatedConcept driftContributing factor · RelatedContributing factorData drift · RelatedData driftDual use · RelatedDual useMachine learning · RelatedMachine learningTransparency · RelatedTransparencyContrast with (1)Explainability · ContrastExplainability
Opacity: related terms and patternsThe term at the centre; related terms on solid edges, the terms it is contrasted with on dashed edges, and the patterns that use it most in the colour of their layer.RelatedContrastRelated terms (7)Catastrophic forgetting · RelatedCatastrophic forgettingConcept drift · RelatedConcept driftContributing factor · RelatedContributing factorData drift · RelatedData driftDual use · RelatedDual useMachine learning · RelatedMachine learningTransparency · RelatedTransparencyContrast with (1)Explainability · ContrastExplainabilityOpacity
The term's neighbourhood Terms related to this one on solid edges, terms it is often confused with on dashed edges, and the patterns that use it most in the colour of their layer. Follow a dashed edge to check the distinction before you use either term.
Neighbourhood as a table
Neighbourhood of Opacity
FamilyItemRelation
Related termsCatastrophic forgettingRelated
Related termsConcept driftRelated
Related termsContributing factorRelated
Related termsData driftRelated
Related termsDual useRelated
Related termsMachine learningRelated
Related termsTransparencyRelated
Contrast withExplainabilityContrast
  • Catastrophic forgetting

    The tendency of neural networks to lose earlier competence when trained on new tasks.

  • 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…

  • Dual use

    The capacity of the same AI capability to serve harmful ends as well as legitimate ones, for example a toxicity model inverted to propose toxic molecules.

  • Transparency

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

  • Contributing factor

    A property of a system or its context (autonomy, exposure, reversibility, vulnerable groups, data sensitivity, opacity) that moves the likelihood or severity…

  • Machine learning

    The branch of AI in which a system improves at a task by learning patterns from data rather than by following rules people wrote.

Sources

  1. [1] "How the machine thinks: Understanding opacity in machine learning algorithms" (three forms of opacity; Big Data & Society 3(1)). SAGE (Jenna Burrell). 2016-01-06. https://doi.org/10.1177/2053951715622512 (verified: primary)

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

Cite this term

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

BibTeX

@misc{aige2026opacity,
  author  = {Jorge García Aibar},
  title   = {{Opacity}},
  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/opacity}
}