The principle "AI Ethics Principles" has mentioned the topic "test" in the following places:

    · 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:

    3 The AI System should be tested to ensure that the combination of available data does not reveal the sensitive data or break the anonymity of the observation.

    · Plan and Design:

    2 The designers of the AI model should define how the AI system will align with fundamental human rights and KSA’s cultural values while designing, building, and testing the technology; as well as how the AI system and its outcomes will strive to achieve and positively contribute to augment and complement human skills and capabilities.

    Prepare Input Data:

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

    Prepare Input Data:

    In this step, stress test data should be prepared for extreme scenarios.

    · Build and Validate:

    1 To develop a sound and functional AI system that is both reliable and safe, the AI system’s technical construct should be accompanied by a comprehensive methodology to test the quality of the predictive data based systems and models according to standard policies and protocols.

    · 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:

    System integration test sign off should be done with relevant stakeholders to minimize risks and liability.

    · Deploy and Monitor:

    Periodic UI and UX testing should be conducted to avoid the risk of confusion, confirmation of biases, or cognitive fatigue of the AI system.

    · Build and Validate:

    3 The decisions should be supported with quantitative (performance measures on train test datasets, consistency of the performance on different sensitive groups, performance comparison for each set of hyperparameters, etc.)

    · Build and Validate:

    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.

    · Build and Validate:

    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.