Purpose
EUAIC is focused on helping organisations make AI use more accountable through system inventory, risk classification, evidence management, human oversight, monitoring and management reporting.
Human accountability
AI governance should not remove human responsibility. Organisations should assign owners, document decisions, define review points, manage exceptions and ensure appropriate people understand their role.
Transparency and evidence
Responsible AI requires records of purpose, context, expected use, limitations, affected people, vendor evidence, approvals, monitoring, incident history and changes over time.
Risk-based governance
AI systems should be reviewed according to their purpose, deployment context, sector, users, affected people, potential impact and applicable legal obligations. Higher-impact systems need stronger evidence and oversight.
Continuous improvement
AI systems, regulations and business uses evolve. Responsible governance should include periodic review, change assessment, incident learning and policy improvement.
Risk and impact review
Responsible AI governance should consider who may be affected, what decisions or recommendations the AI supports, whether humans can challenge or override outputs, how errors are detected and how evidence is retained.
Vendor and model dependency
Where AI depends on third-party models, APIs, datasets or tools, organisations should understand the supplier role, contractual terms, available documentation, limitations, data handling and monitoring responsibilities.
Training and awareness
Staff using AI systems should understand approved use, prohibited use, escalation routes, confidentiality expectations, human review requirements and how to report concerns or unexpected behaviour.