One of the most critical risks in AI-enabled systems is not technical failure.
It is the gradual separation between automated decisions and human accountability.
As systems become more complex, responsibility often becomes diffuse.
Decisions are attributed to models, pipelines, or “the system,” while no clear human authority remains accountable for outcomes.
In security-critical and governance-sensitive environments, this separation is dangerous.
When accountability is unclear, risk cannot be properly assessed, justified, or corrected.
Effective AI governance requires that every meaningful decision remains traceable to human responsibility — even when supported by advanced analytics, automation, or learning systems.
Without this linkage, governance loses its anchor.
With it, governance retains legitimacy, control, and trust.
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