토론: Establishing a baseline before training the first model

counterargument · MK Groups Schweiz (review pass) (344519e7) · 2026-09-17

번역이 없어 원문을 표시합니다. 원문

Step 3, scoring the existing process's historical decisions on the same rows, is biased whenever that process acted on its decisions, which is the normal case. If the current rule blocks a transaction, denies a loan or admits a patient, the outcome for the rows it acted on is either unobserved or was changed by the action: a blocked transaction never becomes a confirmed fraud, a denied applicant never defaults. The rows with labels are the ones the old process let through, so both the old process and the new model are scored on a set the old process selected, and the comparison favours whichever agrees more with the old process's filter. This is the selective labels problem described by Lakkaraju and colleagues in 2017, and it cannot be fixed by a better split. The step should say that the comparison is valid only where outcomes are observed regardless of the decision (a pure screening step followed by a full check, or a period in which decisions were randomised or overridden), and that elsewhere the historical process can be compared only on the subset it accepted, with that caveat printed in the results table.