[vlovich123]

Have the classifier incorporate the identifying characteristics of the minority so that it can push past beyond the 99% limit it's at currently.

This is why people are pissed off about the trolly problem. It's all zero-sum hypotheticals when reality is primarily not zero-sum.

[whyte_mackay]

Adding fairness to your models usually incurs a cost which can be measured. You have to choose between max profit or equal opportunity, you can't have both (do you run over the investors or over the minorities?).

See here a visualization of trade-off's between global accuracy and fairness:

http://research.google.com/bigpicture/attacking-discriminati...

[polkapolka]

The identifying characteristics are signal for the minority, but noisy for genpop.

Deployment/engineering constraints call for a single model.

Realistic scenario.