A university archive approves research-loan requests with a six-rule checklist: requester affiliation, object on exhibit, insurance certificate, condition report, prior damage, and a duration cap. The checklist is stable, and a clerk can apply it in minutes. A vendor proposes a six-month machine learning project to predict approvals from five years of past decisions. Which action should the archive take?
Select an answer to reveal the explanation.
Short Explanation
Think of it like hiring a consultant to predict whether a light switch is on, when six labeled switches already sit on the wall. The archive already has a tiny, stable checklist that gives a clean yes or no in minutes, so a six-month model is spending a fortune to guess what the rules already decide. Clustering and random try-and-see learning would just wander off the handbook.
Full Explanation
Machine learning is a poor fit when a tiny, stable rule set already yields the exact decision the business needs, and when the project cost exceeds that benefit. The archive can apply six published checks in minutes, so a six-month predictive project adds delay and spend without improving correctness. A classifier still estimates a label and would drift if a rule changes. Clustering and reinforcement learning do not apply a mandated checklist and would make loan decisions inconsistent.