How is AI governance different from model monitoring?
Model monitoring measures properties such as accuracy, drift, latency, bias, reliability, or security behavior. Decision governance addresses whether the institution authorized the system's role, whether a consequential decision remained within permitted boundaries, whether evidence was sufficient, who was accountable, and whether the decision record can be examined later.
Model monitoring measures properties such as accuracy, drift, latency, bias, reliability, or security behavior. Decision governance addresses whether the institution authorized the system's role, whether a consequential decision remained within permitted boundaries, whether evidence was sufficient, who was accountable, and whether the decision record can be examined later.
A technically healthy model can still be used for an unauthorized purpose, in the wrong workflow, by the wrong actor, with inadequate evidence or unclear accountability.
- Model and system monitoring appropriate to the use case.•
- Decision rights and authorized remit.•
- Governed workflow and escalation conditions.•
- Contemporaneous decision evidence.•
- Named institutional and clinical accountability.•
Monitoring asks whether the system is performing. Decision governance asks whether the institution can authorize, control, explain, and defend the consequential use of that performance.
Accountable authority: Technology and security leaders oversee monitoring; institutional and clinical authorities govern use and accountability.
Compare approaches on the category comparison page, and see the Governed Decision Record definition. Review the public evidence basis. Review the practitioner and standards context. Return to the Decision Governance library.
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