[dan-robertson]

> If your best performing algorithm is racist

There are two ways to look at this:

1. Racism makes the algorithm good so we should make the algorithm less racist (at a cost to its performance) or decide we want to allow systematic racism.

2. The metric for how good the algorithm is (ie training data) encourages it to be racist and therefore correcting the bias in the algorithm may decrease its performance on the training data but may not affect its performance in the real world, or decrease its performance in the “performance + meets legal requirements” metric.