A veterans benefits administration office uses a GenAI tool to draft initial determinations on disability compensation claims, which a human adjudicator reviews before any decision is finalized. Leadership is now considering removing the human review step for claims where the AI's confidence score is very high, to reduce a growing case backlog. A policy advisor recommends against this change. What is the strongest reason to keep human review even for high-confidence AI determinations?

A model's confidence score measures how certain the model is in its own output, which is not the same as the output actually being correct; a model can be confidently wrong, and for a disability compensation determination that affects a veteran's income and care, an undetected error is hard to catch without an independent human check. Claiming high-confidence determinations are, by definition, always correct treats confidence as a guarantee of correctness, a false equivalence that ignores how confidence scores are actually generated and what they measure. Arguing the backlog is more urgent than the small risk introduced by removing review for high-confidence cases prioritizes reducing the backlog over the risk to individual veterans, understating the real harm of an undetected wrong determination in a high-stakes benefits context. Reserving review only for claims the AI itself flags as uncertain is circular: it relies on the model's own self-assessment of uncertainty to decide when review is needed, which defeats the purpose of independent human oversight as a check specifically against the model's own errors, including ones it's confidently unaware of. Operational check: treat a confidence score as an internal signal about the model, never as a substitute for an independent human check on a determination that materially affects someone's benefits.