Fairness Threshold Policy

Data artifactGovernance & ComplianceSafety, Security & Governancearc:FairnessThresholdPolicy

A policy artifact declaring protected attributes, the prioritized and secondary fairness metrics with their rationale, and acceptable thresholds (fairness SLOs) beyond which investigation and intervention are triggered.

Responsibility. Specifies which fairness metrics apply and the acceptable disparity bounds.

Also known as: Fairness SLO, Fairness metric policy, Acceptable bias thresholds, Ethical AI fairness policy, Bias tolerance thresholds, Fairness metric targets

Direct neighbourhood (hover for relationship types)

Relationships

configures structural

is configured by structural

AI Impact Assessment Record Ch9.4 Ref9.02

Design guidance

MUST select fairness metrics by context, legal obligations, stakeholder values and harm assessment, not mathematical elegance.
SHOULD document transparently which fairness philosophy is prioritized and why alternatives were deprioritized.
SHOULD set achievable mitigation thresholds rather than pursue perfect bias elimination.
MUST identify protected attributes and vulnerable populations before choosing fairness metrics.
SHOULD define a response when bias exceeds acceptable thresholds, up to withdrawal from use.

Quantitative guidance

As stated by the sources; verify before use.

Example fairness SLOs: demographic parity gap below 5 percentage points; false positive rate disparity below 2 percentage points (Ch9.4).
Ref9.02 lists universally protected characteristics: race/ethnicity, gender and gender identity, religion, national origin, age, disability status.
Disparate impact ratio below 80% requires investigation; below 70% requires remediation (Ref9.03).
False-positive-rate difference above 10% requires review (Ref9.03).
Demographic parity target > 0.80; false-positive disparity target < 10% (Ref9.06).
Fairness tests pass when demographic parity, equalized odds and calibration each exceed 0.85 (Ref9.06).

Classification

Quality attributes: Fairness (NIST AI RMF: fair, harmful bias managed)Transparency and accountability (NIST AI RMF: accountable and transparent)Maintainability (ISO/IEC 25010)
Risks mitigated: Disparate impactUndetected emerging biasDisparate impact on protected groups
Frameworks & regulations: Fair Housing ActEqual Credit Opportunity ActCivil Rights ActEEOC adverse impact testingFair lending lawsISO/IEC 42001 Annex A: bias and fairness controlsNIST AI RMF: MEASURE

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.
Ch9.6: 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.
Ch9.8: T. Nguyen, "Standards and Frameworks for AI Governance," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 9.8. 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
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
Ref9.06: "Auditing and Compliance Monitoring for AI Systems," unpublished reference note (references/Chapter 9 - Safety, Ethics, and Compliance/06-Auditing-Compliance-Monitoring.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note