Who Is Actually Accountable When AI Makes the Call?

In many organizations, AI decisions have owners on paper but not in practice. When outcomes go well, the system is praised. When things go wrong, responsibility dissolves into process, data, or “the model.”

This ambiguity is rarely audited. Roles are defined, but accountability is assumed. AI Audit checks governance charts yet skips the moment where no one feels authorized to challenge the system anymore.

Real risk appears when AI decisions affect customers, pricing, or prioritization, and everyone believes someone else is responsible. At that point, escalation paths exist, but escalation courage does not.

AI Audit must surface where accountability thins out, where ownership becomes symbolic, and where decisions outgrow the structures meant to contain them. If you can’t clearly answer who owns an AI decision in real time, then no one truly does.

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