For Heads-of · Practitioner
Disparate impact monitoring gap
No structured monitoring exists to detect disparate outcomes across protected characteristics once an AI system is in production.
- high
- fairness
- bias
- monitoring
How it happens
A model is validated for fairness once, before launch, but nothing tracks whether its outcomes drift into disparate impact across protected characteristics as the input population, product, or model itself changes over time.
Why it matters
A model can pass its pre-launch fairness check and still become discriminatory in production, and without ongoing monitoring that shift is invisible until an individual complaint or audit surfaces it.
Mitigating controls
The controls that address this risk, ranked by effectiveness.
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 |
Related resources
The external sources behind this risk, from the Resources library.