Fairness Is a Product Metric, Not a Paper Section

I spent time on bias-aware deep networks not because fairness is decorative, but because silent disparity is a product defect.

Define the population slices that matter. Without groups, fairness metrics become abstract science fair posters.

Track false positive / false negative rates side by side with overall accuracy. A higher average can hide worse errors on a subgroup.

Interventions (reweighting, constraints, post-processing) need the same rollout discipline as any model change: canary, monitor, rollback.

If leadership only asks “is accuracy up?”, make the fairness chart the next slide every time.