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Testing should be done continuously at each component of the AI system lifecycle. The level of testing at each stage could be at unit, integration, system, or system-of-systems, depending on the scope of development at that stage and the test strategy.

Testing can be divided into formal and informal phases. Formal testing is the phase when the system under test (SUT) is formally versioned, and the outcomes are evaluated for deciding whether to deploy to production or not. Statements within the standard apply to the formal testing phase. 

Where an AI system or components have been procured, deployment and integration into the deployer’s environment may need to be done before starting formal testing.

Testing of AI systems differs from testing traditional software as they can be probabilistic and non-deterministic in nature. While probabilistic systems do not give you an exact value, non-deterministic systems may have different outputs using the same inputs.

Other key differences include your approach to regression testing. While making small changes to a non-AI system may have limited consequences, a step change in test data or in parameters can be significant for an AI system. This means you need to conduct more robust regression testing to mitigate the heightened risk of escaped defects.

Note that an AI system which learns dynamically will change its behaviour without being formally updated. This means changes may occur on the same deployment version and without formal testing. This will require a more rigorous continuous monitoring post deployment. The development team or supplier should confirm whether your AI system has been designed to learn dynamically or statically.
 

Deploying a standalone or integrated AI system into existing IT infrastructure involves assessing compatibility and confirming data interoperability. This may require some reconfiguration to current systems.

Agencies can achieve the best outcomes at this stage by adopting practices that closely align with those implemented at the test stage. This ensures the AI system has been thoroughly tested against its intended purpose prior to integration – a key measure before the AI system potentially contaminates the business environment. See Test section for a detailed discussion on testing methods.

Following recommended practices for managing code integration workflows for AI systems will help agencies to maintain quality, security, and consistency.
 

Deployment involves rigorous testing, governance, and security practices to ensure systems perform as intended in live environments. 

Defining structures for secure deployment of an AI system is particularly crucial in a government setting. Deployment strategies should be incremental, consistent, and non-disruptive. 
 

Monitoring AI systems is critical because changes in the operating environment and inputs could result to degradation and potential harms. Effective monitoring includes continuous performance evaluation, anomaly detection, intervention, and proactive incident response. 

The measures implemented at this stage helps identify if a system is generating outputs that misalign with its intended purpose and promptly remedy issues. 
 

Decommissioning an AI-enabled system encompasses the entire AI production system. It includes retiring, shutting down, or repurposing system components. It is part of the full AI lifecycle process and is distinct from other activities that may occur during the lifecycle like retiring data, AI models, infrastructure, or captured data. 

Taking a structured approach will allow you to safely decommission an AI system – mitigating the risk of unauthorised access or a data breach while ensuring ongoing compliance.