Governance & Compliance · Software component
Decision Factor Explainer
Software componentGovernance & ComplianceSafety, Security & Governancearc:DecisionFactorExplainer
An explanation component that states the specific factors, values and thresholds that drove an automated decision about an individual, so the person can understand and address the decision basis.
Responsibility. Explains automated individual decisions to the affected person.
Also known as: Adverse action notice, Decision explanation, Feature importance explanation, Natural-language decision explainer, Anchor rule explainer, Decision rule audit trail
Variant of Decision Explainer abstract
When to choose. Choose when users need the key factors behind a decision and their relative importance.
Relationships
is invoked by dependency
reads dependency
is routed to by dynamic
receives data from dynamic
- Decision Engine abstract Ch9.7
sends data to dynamic
alternative to variability
Design guidance
- MUST replace cryptic outputs (e.g., 'credit score insufficient') with the factors considered and the approval threshold applied.
- SHOULD show how values guided a specific decision (Ch9.6 explainability).
- SHOULD state the conjunction of factors and thresholds behind each proactive escalation so overseers can validate the logic rather than rubber-stamp it.
- SHOULD translate technical logs into natural-language summaries naming the decisive factors and thresholds instead of raw feature-importance scores.
- SHOULD NOT be relied on alone to calibrate trust; well-articulated explanations can increase automation bias.
Quantitative guidance
As stated by the sources; verify before use.
- Example explanation: debt-to-income ratio 52% exceeds maximum threshold 43%, despite credit score 740 and seven years' stable employment (Ch10.5).
- Fraud anchor example: foreign IP AND amount >300% of typical spending → high risk at 94% confidence (Ch10.5).
Classification
- Patterns
- Algorithmic accountabilityAnchors (minimal sufficient conditions as if-then rules)
- Technologies
- Anchors
- Quality attributes
- Transparency and accountability (NIST AI RMF: accountable and transparent)Explainability (NIST AI RMF: explainable and interpretable)
- Risks mitigated
- Opaque automated decisions
- Frameworks & regulations
- GDPR Art. 22EU AI Act (transparency)GDPR right to explanationEU AI Act transparency obligations for high-risk AIGDPR explanation rights
Sources
- Ch9.6: T. Nguyen, "Value Alignment Frameworks," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 9.6. 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.
- Ch10.2: T. Nguyen, "Proactive Agents," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 10.2. ISBN: 9798244538229.
- Ch10.5: T. Nguyen, "Human-over-the-Loop," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 10.5. ISBN: 9798244538229.
- Ref9.03: "Regulatory Compliance Frameworks for AI Systems," unpublished reference note (references/Chapter 9 - Safety, Ethics, and Compliance/03-Regulatory-Compliance-Frameworks.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note
- Ref10.02: "Explainability and Interpretability in Agent Systems," unpublished reference note (02-Explainability-Interpretability.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note