Demographic Audit Dataset

Data artifactGovernance & ComplianceSafety, Security & Governancearc:DemographicAuditDataset

A test dataset labeled with protected-group membership and covering demographic intersections (e.g., elderly Asian women), used to measure per-group model performance.

Responsibility. Supplies demographically labeled test cases for group-wise fairness measurement.

Also known as: Demographically labeled test set, Fairness test dataset

Direct neighbourhood (hover for relationship types)

Relationships

is read by dependency

Design guidance

MUST define protected groups from regulatory requirements and ethical considerations (e.g., race, gender, age 40+, disability in the U.S.; plus local protections internationally).
SHOULD cover intersections of protected characteristics, not only single attributes.
MUST collect demographic labels ethically, with explicit consent and privacy-preserving aggregation.

Classification

Quality attributes: Fairness (NIST AI RMF: fair, harmful bias managed)Privacy (NIST AI RMF: privacy-enhanced)

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.
Ref9.02: "Responsible AI and Ethical Principles," unpublished reference note (02-Responsible-AI-Ethical-Principles.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note