Governance & Compliance · Software component
Proxy Feature Detector
Software componentGovernance & ComplianceSafety, Security & Governancearc:ProxyFeatureDetector
A bias-analysis component that identifies seemingly neutral input features, such as zip code, names, education credentials, or healthcare cost, that correlate with protected characteristics.
Responsibility. Flags candidate proxy variables for protected attributes for fairness review.
Also known as: Feature fairness evaluation, Proxy variable analysis, Feature selection bias analysis
Relationships
reads dependency
sends data to dynamic
evaluates assurance
Design guidance
- MUST evaluate features for fairness impact, not only task relevance.
- SHOULD route flagged features to human causal review with domain expertise, since automated techniques alone cannot distinguish legitimate from proxy relationships.
- SHOULD be applied during feature selection because output-level principles cannot detect proxy relationships.
Classification
- Quality attributes
- Fairness (NIST AI RMF: fair, harmful bias managed)
- Risks mitigated
- Proxy variable biasFeature selection biasProxy amplification when protected attributes are includedProxy discriminationDisparate impactProxy discrimination (zip code, educational institution)
- Frameworks & regulations
- Equal Credit Opportunity Act (disparate impact)
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.
- Ch9.5: T. Nguyen, "Constitutional AI," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 9.5. 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.