The black box nature of the regression can make things difficult, though. If you have a couple other parameters which indicate 'black male' with high probability, you might find layer one reconstructing the notion of gender and layer two making decisions based on it.
Much better to include the feature in the training, and exclude it from the inference. That actually cancels out the effect, rather than just incentivizing the model to reconstruct it.
