$1-$Another example of how training data can produce sexism in an algorithm occurred a few years ago, when Amazon$$tried to use AI to build a r$$sum$-$screening tool. According to Reuters, the company$$s hope was that technology could make the process of sorting through job applications more efficient. It built a screening algorithm using r$$sum$$s the company had collected for a decade, but those r$$sum$$s tended to come from men. That meant the system, in the end, learned to discriminate against women. It also ended up factoring in proxies for gender, like whether an applicant went to a women$$s college. $($Amazon says the tool was never used and that it was nonfunctional for several reasons.$)$