For Heads-of · Practitioner
Testing AI Systems
Systematic evaluation of AI models and systems to verify they perform as intended, identify failure modes, and assess risks.
- testing
- evaluation
- quality assurance
- verification
Test Types
Unit tests (individual components), integration tests (components together), performance tests (speed and scalability), adversarial tests (robustness to attacks), and fairness tests (performance across groups).
Challenges
AI systems are probabilistic and high-dimensional, making comprehensive testing difficult. Edge cases are common and hard to anticipate. Traditional testing approaches don't capture all risks.
Governance Expectation
Organizations should have rigorous testing protocols in place before deployment, with ongoing testing post-deployment. Red-teaming (adversarial testing) is increasingly expected for high-risk systems.