Lifecycle

Build responsibility into every stage — from idea to retirement — with evidence you can audit

Seven practical stages aligned with RAISEF’s 15 drivers and continuous improvement.

Responsible AI isn’t a single gate. RAISEF weaves the 15 drivers through seven common stages so teams can plan work, gather the right artifacts, and revisit trade-offs as conditions change. Use this lifecycle to see where each driver is primary vs supporting, what “good” looks like at that moment, and which indicators and owners you should put in place before moving on.

Read the Paper

Figure 2: AI lifecycle stages aligned with RAISEF.

Ideation/Proof of Concept

Frame the problem, the people affected, and the intended benefits before any build. Identify relevant RAISEF drivers early, surface key risks, and choose initial indicators you will measure later. A lightweight concept should already show how fairness, privacy, and safety will be considered as the idea matures.

Design

Translate intent into testable requirements, roles, and safeguards. Specify data needs and boundaries, pick explanation and oversight approaches, and plan for security and robustness from the start. Design artifacts link each choice to a RAISEF driver and list the evidence you expect to collect.

Development

Implement with traceability and secure defaults. Curate datasets, control features, and document assumptions so interpretability and bias mitigation remain practical. Code, configs, and data versions are tied to RAISEF drivers to keep ownership and review pathways clear.

Testing

Evaluate behavior against realistic scenarios and agreed thresholds. Include subgroup checks for fairness, stress and adversarial tests for robustness and safety, and dry runs of explanations and human interventions. Results map to indicators so teams can decide what is ready and what needs work.

Deployment

Release in a controlled way with change approval, logs, and rollbacks. Privacy controls, access policies, and guardrails are enforced, and user-facing disclosures tell people what to expect. Evidence from testing moves into production dashboards so accountability continues after launch.

Monitoring

Watch performance, drift, and incidents in real time and over time. Capture user feedback, measure indicators on schedule, and trigger human oversight when risk or uncertainty rises. Findings feed back into fixes, documentation, and updates to the scorecard.

End of Life/Decommissioning

Retire or replace the system in a planned and transparent manner. Archive or delete data according to policy, notify affected stakeholders, and record lessons learned against the relevant drivers. A clean shutdown protects people, preserves evidence, and prepares the ground for safer successors.

Case Studies

Case Study 1: AI-Driven Healthcare Diagnostics

Case Study 2: Fairness in AI-Driven Credit Scoring

Get in touch

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support@raisef.ai
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CANADA

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