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We treat responsible AI as an operating discipline — policy, risk classification, human oversight, evaluation, monitoring and audit evidence woven into delivery.
AI should serve a clear decision or outcome, with methods proportionate to risk and benefit.
People remain accountable for high-impact decisions, exceptions and escalations.
Stakeholders should understand where AI is used, what it influences and how to seek review.
Data minimisation, access control and secure deployment are defaults, not add-ons.
Systems affecting people or public programmes require appropriate testing and documentation.
Monitoring, incident response and retirement pathways keep systems trustworthy over time.