When do we trust the model, and when do we trust ourselves?
You sit with the model's output and something in you resists it. You suspect bias. Fair enough — the model was built by people, trained on the past, and the past was not fair. But notice: you are also built by people, trained on the past, and not fair either. So the real question is not "is the model biased." Of course it is. The question is whether your override, right now, comes from evidence or from comfort. A feeling of unease is not a reason. A pattern you can point to is. This is not a problem you solve alone, at your desk, in the moment of decision. It is solved before that moment, together — with a written standard the whole pod agreed to when no single case was on the line. What is in your power is not a perfectly unbiased model. It is a disciplined, shared rule for when you depart from it.
You override when you can name the specific reason, in writing, before you act — not when something feels off. The model's bias will not be fixed by your gut. It is managed by a rule your team sets in advance and applies the same way every time.
What changes unlock by starting
- The pod has one written standard for overriding the model, not five private instincts.
- Every override is logged and reviewable, not buried in someone's memory.
- The team catches its own bias patterns before a client or regulator does.
- Disagreement with the model becomes a disciplined habit, not an emotional reflex.