AI lifecycle

The sequence of stages covering planning, data collection, development, testing, deployment, monitoring and decommissioning of AI systems, used by the standard to map responsibilities, assessments, testing and accessibility/equity controls across an AI system's life.

The sequence of phases including planning and design, data collection and model construction, verification and validation, deployment, and operation/monitoring that constitute development and use of an AI system; the lifecycle framing anchors documentation, risk management and traceability obligations.

The full set of stages in an AI system's existence, encompassing data collection, hosting, engineering, development, testing, deployment, operation, monitoring, and maintenance. The Charter requires that obligations and safeguards apply across this entire lifecycle.

The set of stages for AI projects as specified by the Framework—strategy, planning, development, deployment and operation—used to structure governance, risk controls, testing, monitoring and documentation across an AI application's lifespan.

The sequence of stages in the development and operational use of an AI system, typically including Planning & Design; Data Collection & Processing; Model Building; Verification & Validation; Deployment; Operation & Monitoring; and Retire/Decommission. The Guidelines map responsibilities and required controls to each stage to ensure governance, testing, monitoring and eventual safe retirement.

The sequence of phases that constitute an AI system's lifespan, explicitly defined as design, development, testing, deployment, monitoring, and retirement, used to structure governance, risk assessment and operational controls throughout the system's existence.

The end-to-end sequence of stages in an AI system's existence—scoping, design, development, testing/validation, deployment, monitoring, maintenance and retirement—used to structure risk controls, documentation and governance activities throughout the system's operational life.

The iterative set of phases an AI system passes through, including design, data collection, model development, testing, deployment, monitoring and decommissioning, used to structure risk management, testing and governance activities throughout a system's operational life.

The end-to-end sequence of activities for an AI system, including planning, procurement, development/configuration, testing/validation, deployment, monitoring, and decommissioning. The policy requires governance and controls to be applied across each lifecycle stage.

The AI lifecycle encompasses the stages of conception, design, procurement, development, deployment, operation, monitoring and decommissioning for an AI solution. FAIRA requires assessments to consider risks and controls across all these lifecycle stages and to re-assess when significant changes occur.

The end-to-end stages of an AI solution, including conception, design, development, procurement, implementation, operation, monitoring, evaluation and decommissioning, for which the policy mandates structured governance, risk assessment and ongoing oversight.

Encompasses research, design, development, deployment, use, maintenance, and decommissioning of AI systems.

The comprehensive stages involved in the development and deployment of an AI system, including design, data and modelling, development and validation, deployment, monitoring, and refinement. Ethical and risk considerations must be applied throughout all these stages.

The sequence of stages for an AI system, including problem scoping, data preparation, model design and development, testing, deployment, monitoring and decommissioning, which the Guidelines require firms to manage and document.

Encompasses the entire process from conception and design through development, deployment, operation, maintenance, and eventual decommissioning of an AI system.

The document also implicitly defines the 'AI Lifecycle' through its application of risk management across various stages, from design and development to deployment and post-deployment monitoring.

The entire process of an AI system, encompassing stages from initial concept and data collection through development, deployment, operational monitoring, incident handling, and eventual retirement.