Agentic AI Reference Architecture

Governance & Compliance · Software component

Privacy-Preserving Fairness Auditor

Software componentGovernance & ComplianceSafety, Security & GovernanceVariation point (abstract)arc:PrivacyPreservingFairnessAuditor

An abstract fairness-audit component that computes group fairness metrics while preventing identification of individuals' demographic data or outcomes.

Responsibility. Produces aggregate fairness metrics without exposing individual-level sensitive data.

Also known as: Privacy-preserving fairness audit

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Direct neighbourhood (hover for relationship types)

Variants

VariantWhen to choose
Differentially Private Fairness AuditorChoose when fairness metrics must be released with provable guarantees that no individual's participation or outcome can be inferred, and sample sizes are large enough to tolerate added noise.
Federated Fairness AuditorChoose when several institutions (hospital networks, bank consortia) individually lack the data volume or diversity for statistically powerful bias detection but cannot centralize raw data.
Secure Multiparty Fairness AuditorChoose when parties must jointly compute aggregate fairness metrics while no party may learn any other party's demographic data or predictions, accepting cryptographic overhead.

Relationships

reads dependency

evaluates assurance

produces lifecycle

Design guidance

Classification

Quality attributes
Privacy (NIST AI RMF: privacy-enhanced)Fairness (NIST AI RMF: fair, harmful bias managed)
Risks mitigated
Privacy-fairness dilemmaExposure of sensitive demographic data through fairness audits
Frameworks & regulations
GDPR (data minimization)HIPAA

Sources

  1. 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.
  2. Ref9.02: "Responsible AI and Ethical Principles," unpublished reference note (02-Responsible-AI-Ethical-Principles.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note