Human Override vs AI Autonomy: Recording an Accountable Review Decision

This is a narrower question than "should AI or humans make compliance decisions" — that's covered elsewhere. Here, the reviewer's authority to override isn't in question; what matters is the mechanics of the override record itself, because a badly recorded override is nearly as damaging to defensibility as no review at all.

The failure mode: silent overwrite

The simplest way to implement a correction is also the most damaging one: the reviewer edits the field, the new value replaces the old one, and the original AI output is gone. Six months later, an experienced reviewer with no prior connection to the file — the standard ISA 230 documentation is meant to satisfy — has no way to tell whether a classification was always correct, was corrected by a human, or was corrected incorrectly. The override becomes indistinguishable from the original.

What a proper override record contains

Under the Dual-Layer Evidence Model, the system's original output and the reviewer's decision are two separate, both-preserved records — not one field that gets overwritten by the other. A minimal override record needs four things to be useful later: what the system originally produced, what the reviewer changed it to, who made that decision and when, and why.

Note what didn't happen: extracted_category was never changed to "Staff Welfare." It still says "Entertainment," exactly as the system read it. The correction lives entirely in Layer 2, linked to Layer 1 rather than replacing it. Anyone reviewing this later sees both the original read and the professional judgment that revised it — which is the actual point of keeping a trail at all.

Where the override boundary sits

This mechanism records that a reviewer overrode a classification and why — it doesn't itself determine who is authorized to make that call, or what happens when a reviewer's own conclusion is later questioned. Those are organisational-authority questions, covered separately.

Continue reading

• Audit Evidence for Malaysian Receipt Workflows: Why OCR Alone Is Not Enough — the full Dual-Layer Evidence Model definition this article applies.
• Why Audit Trails Matter More Than AI Confidence — what the resulting event history can and can't establish.
• From Fragmented Evidence to Review-Ready Working Papers — how an override record like this one becomes part of a documented conclusion.
• Who Owns the Review Decision? Accountability in AI-Assisted Evidence Workflows — who is authorized to record layer 2, and what happens when that decision itself is questioned.