If you know in advance that Primate vs. Human confusions are going to be a potential issue that upset a lot of people (say, because it already happened with a s

If you know in advance that Primate vs. Human confusions are going to be a potential issue that upset a lot of people (say, because it already happened with a similar system at another company) there are certainly ways of trying to ensure that same confusion doesn't happen. Ways to actually fix the problem: 1. Probe the decision boundary between these two classes in your training and test sets. I.E. look for the humans closest to being misclassified as primates. You could probably quickly get a sense if you are near/at risk of making this misclassification. You may also possibly find 1 or 2 mislabeled examples that are throwing things off and can be corrected. 2. Boost your training set by labeling more unlabeled images that are near this decision boundary. So my point was just: this was an issue that could have been anticipated because it has already happened before. It is unfortunate that this issue came up when there are things you can do (such as the steps above), which I believe could have totally squashed this issue with enough iteration. I can't know for sure that this is not what the Facebook team did. If they did take care to try and avoid this harmful model confusion, then I would be very surprised given my experience with deep learning and computer vision. At a minimum, it would have been pretty trivial and a low impact to users to remove the primate class if they didn't have the time/bandwidth to really investigate this more thoroughly as I've described. You can try to argue "hey no one should get upset about this confusion because computer vision is hard and mistakes happen", but I don't think that's a very solid argument either given the long and relatively recent history of racists misclassifying a particular subset of humans as primates.