RESEARCH · AI GOVERNANCE
In a few words
Evidence-based governance connects what an organization says should happen with observable artifacts: which systems exist, who owns them, what controls operate, when a person intervenes and how a consequential decision can be reconstructed.
UNDERSTAND MORE
EXAMPLE
A board, customer or auditor may ask which AI systems are active, who is accountable and what evidence shows oversight exists. A general policy does not answer by itself. The organization needs current records, owners, decisions and controls.
Both matter. Evidence lets people review whether the system works as claimed, find gaps and assign accountability before external pressure forces the issue.
Inventory, purpose, data, owners and risk context are a practical starting point. Discipline should increase with consequence: more impact, uncertainty or irreversibility calls for more evidence and review.
Making an evidence gap visible is not a legal or regulatory compliance declaration. The first goal is to know what can be demonstrated today and what should exist next.
FROM INSIGHT TO ACTION
The best first intervention is not always another policy. Sometimes it is discovering which evidence is missing, who should steward it and which decision cannot yet be reconstructed.