Word embedding models are kicking up some interesting debates at the confluence of ethics, semantics, computer science, and structuralism. Here I want to lay out some of the elements in one recent place that debate has been taking place inside computer science.
I've been chewing on this paper out of Princeton and Bath on bias and word embedding algorithms. (Link is to a blog post description that includes the draft). It stands in an interesting relation to this paper out of BU and Microsoft Research, which presents many similar findings but also a debiasing algorithm similar to (but better than) the one I'd used to find "gendered synonyms" in a gender-neutralized model. (I've since gotten a chance to talk in person to the second team, so I'm reflecting primarily on the first paper here).