Selected features should be verified with business owners and non technical teams.

· 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.

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.

throughout the AI System Lifecycle; to be built in a safe way that respects the privacy of the data collected as well as upholds the highest levels of data security processes and procedures to keep the data confidential preventing data and system breaches which could lead to reputational, psychological, financial, professional, or other types of harm.

· 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.

1 After the deployment of the AI system, when its outcomes are realized, there must be continuous monitoring to ensure that the AI system is privacy preserving, safe and secure.

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.

· Build and validate:

· Build and validate:

Principle 5 – Reliability & safety

The reliability and safety principle ensures that the AI system adheres to the set specifications and that the AI system behaves exactly as its designers intended and anticipated.

On the other hand, safety is a measure of how the AI system does not pose a risk of harm or danger to society and individuals.

A reliable working system should be safe by not posing a danger to society and should have built in mechanisms to prevent harm.

3 Establishing a set of standards and protocols for assessing the reliability of an AI system is necessary to secure the safety of the system’s algorithm and data output.

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

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

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.

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.

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.

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.

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

2 The model must also be monitored in a periodic and continuous manner to verify whether

The AI system must also be safe to prevent destructive use to exploit its data and results to harm entities, individuals, or groups.

· Build and validate:

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

3 It is essential to build and design a human controlled AI system where decisions on the processes and functionality of the technology are monitored and executed, and are susceptible to intervention from authorized users.

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.

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.)

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.

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.

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.