So, it depends on what you want to do here. If the task is just "predict if the person is a software engineer". I'd say go ahead, bias it away. Here, anything t

So, it depends on what you want to do here. If the task is just "predict if the person is a software engineer". I'd say go ahead, bias it away. Here, anything that boosts accuracy is game to me. But if the task is say the pre-screening side. This becomes a more ethically/morally tricky question. If and only if that sex is not a predictive factor for engineer quality, you would then expect to see similar classifier performance for male / female samples. Given that assumption, significant (hah) divergence from equal performance would be something to correct. Of course there are other issues to handle, such as the unbalanced state of the dataset and so on.