A model can score well in evaluation and still be a poor choice for a decision people must explain. Performance, explainability and oversight are not three independent boxes to tick; they shape the same workflow.
A reviewer is shown a model score and three highlighted features but has no authority to challenge the recommendation. That is a display, not oversight. Effective human review requires context, time, a viable alternative and a documented path for correcting a wrong outcome.
Start with the consequence
If a recommendation affects access, safety or a significant financial outcome, define who can review it and what evidence they need. A less complex model with a clear reason may be preferable when it gives adequate performance and supports accountability.
A human reviewer can also introduce inconsistency. Train and calibrate review decisions, then compare outcomes and overrides across teams. If people routinely disagree with a model for a valid local reason, that may point to missing data or an inappropriate objective. Oversight should create learning in both directions.
Test explanations with users
Can the responsible person understand the factors that matter, notice a bad input and challenge an output? A chart labeled “explainability” is not useful if it encourages false confidence. Evaluate errors across relevant groups and conditions.
Keep a human route that works
Give reviewers time, authority and information to override. Record decisions and monitor whether people automatically accept the model or routinely ignore it. Oversight is a real operating capability, not a button placed on a screen.
