Linked.Archi ML-Specific Quality Attributes

Cross-reference for Linked.Archi ML-Specific Quality Attributes classes, object properties and data properties back to ToC

This section provides details for each class and property defined by Linked.Archi ML-Specific Quality Attributes.

Annotation Properties

created
creator
definition
description
license
modified
pref Label
preferred Namespace Prefix
preferred Namespace Uri
publisher
scope Note
source
status
title
was Derived From

created^ap back to ToC or Annotation Property ToC

IRI: http://purl.org/dc/terms/created

creator^ap back to ToC or Annotation Property ToC

IRI: http://purl.org/dc/terms/creator

definition^ap back to ToC or Annotation Property ToC

IRI: http://www.w3.org/2004/02/skos/core#definition

description^ap back to ToC or Annotation Property ToC

IRI: http://purl.org/dc/terms/description

license^ap back to ToC or Annotation Property ToC

IRI: http://creativecommons.org/ns#license

modified^ap back to ToC or Annotation Property ToC

IRI: http://purl.org/dc/terms/modified

pref Label^ap back to ToC or Annotation Property ToC

IRI: http://www.w3.org/2004/02/skos/core#prefLabel

preferred Namespace Prefix^ap back to ToC or Annotation Property ToC

IRI: http://purl.org/vocab/vann/preferredNamespacePrefix

preferred Namespace Uri^ap back to ToC or Annotation Property ToC

IRI: http://purl.org/vocab/vann/preferredNamespaceUri

publisher^ap back to ToC or Annotation Property ToC

IRI: http://purl.org/dc/terms/publisher

scope Note^ap back to ToC or Annotation Property ToC

IRI: http://www.w3.org/2004/02/skos/core#scopeNote

source^ap back to ToC or Annotation Property ToC

IRI: http://purl.org/dc/terms/source

status^ap back to ToC or Annotation Property ToC

IRI: http://purl.org/ontology/bibo/status

title^ap back to ToC or Annotation Property ToC

IRI: http://purl.org/dc/terms/title

was Derived From^ap back to ToC or Annotation Property ToC

IRI: http://www.w3.org/ns/prov#wasDerivedFrom

Adversarial Robustnessⁿⁱ back to ToC or Named Individual ToC

IRI: https://meta.linked.archi/ml-systems/quality-attributes#AdversarialRobustness

The degree to which an ML model maintains correct behavior when subjected to adversarial inputs — deliberately crafted perturbations designed to cause misclassification. Includes robustness against evasion attacks, data poisoning, and model extraction.

belongs to: Quality Attribute ^c
has facts: definition ^ap "The degree to which an ML model maintains correct behavior when subjected to adversarial inputs — deliberately crafted perturbations designed to cause misclassification. Includes robustness against evasion attacks, data poisoning, and model extraction."@en; pref Label ^ap "Adversarial Robustness"@en

Data Privacy (ML)ⁿⁱ back to ToC or Named Individual ToC

IRI: https://meta.linked.archi/ml-systems/quality-attributes#DataPrivacy

The degree to which an ML system protects the privacy of individuals whose data was used for training. Includes resistance to model inversion attacks, membership inference attacks, and training data memorization.

belongs to: Quality Attribute ^c
has facts: definition ^ap "The degree to which an ML system protects the privacy of individuals whose data was used for training. Includes resistance to model inversion attacks, membership inference attacks, and training data memorization."@en; pref Label ^ap "Data Privacy (ML)"@en; scope Note ^ap "Extends general privacy concerns with ML-specific attack vectors."@en

Explainabilityⁿⁱ back to ToC or Named Individual ToC

IRI: https://meta.linked.archi/ml-systems/quality-attributes#Explainability

The degree to which an ML model's predictions can be understood, interpreted, and communicated to stakeholders. Includes local explanations (why this specific prediction) and global explanations (how the model generally behaves). Critical for regulatory compliance (GDPR Article 22), stakeholder trust, and debugging.

belongs to: Quality Attribute ^c
has facts: definition ^ap "The degree to which an ML model's predictions can be understood, interpreted, and communicated to stakeholders. Includes local explanations (why this specific prediction) and global explanations (how the model generally behaves). Critical for regulatory compliance (GDPR Article 22), stakeholder trust, and debugging."@en; pref Label ^ap "Explainability"@en; scope Note ^ap "Also known as Interpretability. Related to Trustworthiness."@en

Fairnessⁿⁱ back to ToC or Named Individual ToC

IRI: https://meta.linked.archi/ml-systems/quality-attributes#Fairness

The degree to which an ML model treats individuals or groups equitably, without systematic discrimination based on protected attributes. Includes demographic parity, equalized odds, and individual fairness metrics.

belongs to: Quality Attribute ^c
has facts: definition ^ap "The degree to which an ML model treats individuals or groups equitably, without systematic discrimination based on protected attributes. Includes demographic parity, equalized odds, and individual fairness metrics."@en; pref Label ^ap "Fairness"@en

ML Monitorabilityⁿⁱ back to ToC or Named Individual ToC

IRI: https://meta.linked.archi/ml-systems/quality-attributes#MLMonitorability

The degree to which an ML model's production behavior can be observed, measured, and alerted on. Includes prediction distribution monitoring, feature drift detection, data quality checks, and serving performance tracking.

belongs to: Quality Attribute ^c
has facts: definition ^ap "The degree to which an ML model's production behavior can be observed, measured, and alerted on. Includes prediction distribution monitoring, feature drift detection, data quality checks, and serving performance tracking."@en; pref Label ^ap "ML Monitorability"@en; scope Note ^ap "Identified by Lewis et al. (2021) as a new quality attribute concern specific to ML-enabled systems."@en

Model Freshnessⁿⁱ back to ToC or Named Individual ToC

IRI: https://meta.linked.archi/ml-systems/quality-attributes#ModelFreshness

The degree to which an ML model reflects current real-world conditions. Inversely related to model staleness — a model trained on old data may not capture recent distribution shifts.

belongs to: Quality Attribute ^c
has facts: definition ^ap "The degree to which an ML model reflects current real-world conditions. Inversely related to model staleness — a model trained on old data may not capture recent distribution shifts."@en; pref Label ^ap "Model Freshness"@en

Reproducibilityⁿⁱ back to ToC or Named Individual ToC

IRI: https://meta.linked.archi/ml-systems/quality-attributes#Reproducibility

The degree to which an ML experiment or training run can be exactly reproduced given the same data, code, and configuration. Requires versioning of data, code, hyperparameters, and environment.

belongs to: Quality Attribute ^c
has facts: definition ^ap "The degree to which an ML experiment or training run can be exactly reproduced given the same data, code, and configuration. Requires versioning of data, code, hyperparameters, and environment."@en; pref Label ^ap "Reproducibility"@en

Trustworthinessⁿⁱ back to ToC or Named Individual ToC

IRI: https://meta.linked.archi/ml-systems/quality-attributes#Trustworthiness

The degree to which stakeholders can rely on an ML system's predictions and behavior. A composite quality attribute encompassing explainability, fairness, robustness, privacy, and accountability.

belongs to: Quality Attribute ^c
has facts: definition ^ap "The degree to which stakeholders can rely on an ML system's predictions and behavior. A composite quality attribute encompassing explainability, fairness, robustness, privacy, and accountability."@en; pref Label ^ap "Trustworthiness"@en

Legend back to ToC

ⁿⁱ: Named Individuals

Linked.Archi ML-Specific Quality Attributes

Release: 2026-05-03