· Build and validate:

1 At the build and validate stage of the AI System Lifecycle, it is essential to take into consideration implementation fairness as a common theme when building, testing, and implementing the AI system.

· Build and validate:

· Build and validate:

· Build and validate:

2 It is crucial for the build and validate step to test how the system behaves under outlier events, extreme parameters, etc.

· Build and validate:

2 To ensure the technical robustness of an AI system rigorous testing, validation, and re assessment as well as the integration of adequate mechanisms of oversight and controls into its development is required.

· Build and validate:

The data preparation process and data quality checks should be documented and validated by responsible parties.

· Build and validate:

To achieve this, the technical stakeholders who build and validate models should be responsible for these decisions.

4 The appropriate stakeholders and owners of the AI technology should review and sign off the model after successful testing and validation of user acceptance testing rounds have been conducted and completed before the AI models can be productionized.

The outcomes and decisions set in the build and validate step should be monitored continuously and should result in periodic performance reports.