AI system life cycle

A structured sequence of stages for AI systems defined by the strategy — plan & design; data collection and processing; model building/adaptation; testing/validation; deployment; operation/monitoring; and decommissioning — used to organise governance, documentation, risk assessments and oversight across an AI system's existence.

A lifecycle framing (adopted from the UNESCO Recommendation) covering stages of an AI system including research, design, development, procurement, deployment, maintenance, monitoring, evaluation and end‑of‑use; used to guide where ethical responsibilities and governance measures should apply throughout an AI system’s existence.

The entire progression of an Artificial Intelligence system from its initial idea conception to its eventual decommissioning, encompassing all stages of data processing and system operation.

This encompasses all stages of an AI system's existence, including requirements, data readiness, model engineering, verification, validation, deployment, monitoring, and eventual retirement, integrating AI-specific considerations into established software and system life cycle models.

The AI system life cycle encompasses all phases of an artificial intelligence system's existence, including initial conception, design, development, training, testing, deployment, operation, monitoring, maintenance, and eventual retirement. This holistic view ensures that considerations like explainability and risk management are integrated throughout the entire lifespan of the AI system.