Cognition · Model asset

Predictive Decision Model

Model assetCognitionCognition & Memoryarc:PredictiveDecisionModel

Trained classifier or scoring model weights whose predictions (e.g., loan approval, hiring screen, diagnosis, risk score) drive consequential decisions about individuals.

Responsibility. Predicts an outcome or risk score for an individual case.

Also known as: Decision model, Risk scoring model, Loan approval model, Hiring algorithm, Diagnostic model, Credit decisioning model, Claims fraud detection model, Recruitment screening model

Direct neighbourhood (hover for relationship types)

Relationships

deployed on structural

Decision Engine abstract Ch9.7
Inference Server Ch9.4

is evaluated by assurance

is monitored by assurance

Fairness Monitor Ch9.4 Ref9.02

is trained by lifecycle

Fairness-Constrained Trainer Ch9.4 Ref9.02

Design guidance

MUST NOT be treated as objective because it is algorithmic; every deployment requires bias testing.
SHOULD NOT rely on target variables (e.g., healthcare cost as a proxy for medical need) that encode historical inequity.
SHOULD NOT include or exclude protected attributes categorically; decide via causal analysis of proxy relationships.
SHOULD be retrained on economically or job-relevant factors only when proxies for protected characteristics are found.

Quantitative guidance

As stated by the sources; verify before use.

Obermeyer (2019) case: at equal true medical need, Black patients were selected for high-risk care management at 17.7% vs. 46.5% for white patients (Ch9.4).
Facial recognition example: 99% overall accuracy but 65% for dark-skinned women (Ch9.4).
Illustrative: rebalancing raised dark-skinned accuracy from 68% to 93% and overall accuracy from 85% to 95% (Ch9.4).

Classification

Quality attributes: Fairness (NIST AI RMF: fair, harmful bias managed)Functional suitability: correctness and validity (ISO/IEC 25010 | NIST AI RMF: valid)
Frameworks & regulations: Fair Housing Act (disparate impact liability)Equal Credit Opportunity ActCivil Rights ActGDPR Art. 22EU AI Act (high-risk requirements)

Sources

Ch9.4: T. Nguyen, "Fairness and Bias Mitigation," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 9.4. ISBN: 9798244538229.
Ch9.7: T. Nguyen, "GDPR and Data Protection Regulations," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 9.7. ISBN: 9798244538229.