In terms of the technical effect that causes it as opposed to sociological. The point then isn't that the algorithm hates black people or the programmers are r

In terms of the technical effect that causes it as opposed to sociological. The point then isn't that the algorithm hates black people or the programmers are racist (even if they were they would likely find it hard to train it to specifically exclude accurately without major side effects), but that their training set or analysis is flawed - potentially even if it is representative. Even if the results are problematic trying to address hate which isn't there won't be helpful. Call for better vetting procedures instead or that it clearly isn't ready for the proposed application. Say it gets the highest accuracy on a set of photos of non-people images and a set of people which represents the exact ethnic make up all tagged just as "has people" or "doesn't have people". Going with something biased towards the most populous groups would get the best results quickest. Talking about say image tagging to ensure automated performance checking across various characteristics could be productive on the other hand.