Verifying and validating algorithm performance is a crucial aspect of AI system development and quality control.
In AI systems with machine learning components, verification will require comprehensive validation testing in addition to theoretical and analytical verification. In many cases, validation will require that the developer organization builds a simulation environment where it can explore algorithm performance using comprehensive samples of real-world, non-training data inputs. Further, validation may require developing post hoc interpretability tools to gain insight into algorithm logic.
The Algorithm Owner should ensure that the organization develops appropriate verification and validation methods to ensure adequate algorithm performance.