>That's just reality. Except it really isn't. If the datasets used, truly represented everyone in the world, that would be a reasonable argument. The point is

>That's just reality. Except it really isn't. If the datasets used, truly represented everyone in the world, that would be a reasonable argument. The point is that right now, the most cheaply available and voluminous data sets online tend to have a whole bunch of examples from western nations and far fewer from other parts of the world, for the simple reason that historically most of the people taking photographs and sticking them on webservers were from those places. "Reality" doesn't have the same statistical anomalies as these data sets (e.g. there are a hell of a lot more people with brown skin in the world than are included in common training data), so "that's just reality" really isn't a strong argument. This is a very, very common problem in ML and isn't limited to politically-charged words. For example, in some of the earliest attempts at using computer vision to detect tanks in an image for military purposes, the photographs with tanks in them all had different lighting than the ones without tanks and so the (super simplistic ML model) overfit the model based on a bias in the data. Unless the data set is truly representative, you'll often get biases in the resultant model. > If you have your own set of politically correct answers hardcoded by a team of blue haired people you're not doing machine learning. Well, this is just silly. We all know Deepmind has a policy of only allowing green hair dye on campus.