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.

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.

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.

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.

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