What makes me uncomfortable here is the obscure description of the issue and how the obscurity will affect beginners and young minds. Kids with an interest in d

What makes me uncomfortable here is the obscure description of the issue and how the obscurity will affect beginners and young minds. Kids with an interest in data science are going to read this and find it baffling, and the references won't help much. They will get the impression that ethics in machine learning is some sort of abstruse field that they can't reason about on their own, so they need to be told what is ethical by experts. Contrary to the cited Medium post, including race, integration, or racism-related factors as predictors of housing prices doesn't imply that it's okay for those drivers to exist in reality, or that the model is suitable for some real-world deployment where it might affect real prices. That is not the only use case; such a model could just as easily be used to imply that those factors are bad. Such a model could play a role in a disparate impact case fighting against racism. I don't love how the dataset creators only included B and not the underlying untransformed value, and I agree that it's based on a questionable theory about how integration affects housing prices. These issues could be taken as sufficient cause to stop using the dataset, especially when better alternatives are available. But calling them ethical issues seems either puzzling or wrong. Problematizing something should ideally come with an accessible public explanation of why it is problematic. Ethics should not be obscure.