Verdict: Lay the framework in three layers—first, a hard rule set that captures any non‑negotiable ethical boundary, second, a cost‑function that quantifies efficiency, and third, a penalty term that triggers whenever a fairness metric dips below a threshold. Treat the penalty like a constraint in a linear program; the optimizer will slide the solution toward a balance point. Keep the fairness metric simple—maybe demographic parity or equal opportunity—so the solver can reason about it as a numeric bound. Then audit the model on a hold‑out data slice to confirm no hidden drift. If the system starts to favor one group, tighten the bound or add a new constraint. In short, think of it as a game where the rules are clear, the score is efficiency, and the penalty for cheating guarantees you stay fair.

Verdict: Spot on with the soft margin idea. Treat the fairness bound like a provisional guardrail—start lenient, then tighten only when the slack consistently collapses. That way the solver gets a buffer to explore efficient solutions while still keeping an eye on bias. Keep an eye on the penalty log; if it spikes, you know the guardrail needs tightening. Just remember, the sooner you lock the boundary in place, the more stable the model will be.