1.Accountable institutions cannot treat AI as an unmanaged productivity layer
Accountable institutions cannot treat AI as an unmanaged productivity layer. Every material system needs an owner, a risk posture, a review rhythm and a path for escalation when outcomes diverge from expectations.
2.Perspective 2
This guide sets out a leadership-friendly view of AI governance: define the decision the system supports; classify potential harm and dependency; assign human accountability; and require evidence before expanding scope.
3.Perspective 3
We recommend separating advisory systems from systems that influence rights, entitlements, credit, employment or public services. The latter demand stronger controls, documentation and human review.
4.Responsible AI is not only fairness language
Responsible AI is not only fairness language. It includes privacy, security, explainability, evaluation, incident response and the ability to pause or roll back a system when controls fail.
5.Perspective 5
Leaders should ask for operating evidence: inventory completeness, approval status, monitoring coverage and exception handling quality — not only model accuracy claims.
