For Heads-of · Practitioner
Bias testing and fairness monitoring
Pre-deployment subgroup performance testing plus ongoing production monitoring for disparate outcomes across protected characteristics.
- detective
- fairness
- bias
- monitoring
What it does
Tests a model's performance and decisions broken down by protected characteristic before launch, then continues monitoring the same breakdown in production to catch drift into disparate impact.
Where it fits
Mitigates disparate-impact-monitoring-gap, proxy-discrimination-via-correlated-features, and inadequate-fairness-testing-before-deployment together.
Risks this mitigates
The risks this control addresses, ranked by effectiveness.
Inadequate fairness testing before deployment
An AI system used in a high-risk domain is deployed without pre-deployment testing for disparate performance or outcomes across demographic groups.
Proxy discrimination via correlated features
A model achieves discriminatory outcomes indirectly, through features that correlate with a protected characteristic even when that characteristic is excluded from the input.
Disparate impact monitoring gap
No structured monitoring exists to detect disparate outcomes across protected characteristics once an AI system is in production.
Framework and clause references
| Framework | Clause | Title |
|---|---|---|
| ISO/IEC 42001:2023 | Annex A.5 | Assessing impacts of AI systems |
| NIST Generative AI Profile (NIST AI 600-1) | Harmful Bias | Harmful Bias or Homogenization |