Q20 of 21 · Testing AI systems
How do you approach regulatory and compliance testing for a high-risk AI system?
Short answer
Short answer: Map the system to applicable frameworks (EU AI Act risk tiers, NIST AI RMF, sector-specific regulation), identify which testing obligations apply, build them into the pre-release process, and produce the documentation (test logs, bias evaluations, conformity assessments) each framework requires.
Detail
High-risk AI systems — those making decisions about employment, credit, healthcare, biometric identification, or critical infrastructure — face specific regulatory testing obligations beyond functional quality.
EU AI Act: high-risk systems require conformity assessment including data governance testing, robustness and accuracy testing, human oversight testing, and a technical documentation package. Test logs and evaluation results become part of the conformity evidence.
NIST AI RMF: the Measure function requires quantitative testing of risk — bias metrics, reliability statistics, robustness under adversarial conditions. The Govern function requires documented roles and responsibilities for AI risk management.
Practical integration:
- Assign a compliance owner who maps each regulatory requirement to a specific test artefact.
- Integrate required tests into the release gate — bias evaluation, robustness test, HITL verification — with pass criteria documented.
- Version-control the required documentation (model cards, data sheets, test logs) alongside the software release.
- Schedule post-deployment monitoring that feeds back into the compliance evidence base.
See NIST AI RMF in practice and Audit trails and model cards.