First, some definitions: Equality of opportunity: everyone takes the same test, outcome is dependent on individual, even if the group containning that individu

First, some definitions: Equality of opportunity: everyone takes the same test, outcome is dependent on individual, even if the group containning that individual scores differently than other groups Equality of outcomes: everyone takes the same test, but the individuals scores are weighted based on the group of which they are a member, so that representation of groups is equivalent across all groups. Further, we can separate bias in terms of intentional and unintentional, where the former indicates direct manipulation of selection criteria in order to change group outcomes to match the manipulator's desires. The latter occurs when systematic bias affects the individual's scores, such as when a particular group has less access to educational opportunities. The article values equality of outcomes, by encouraging algorithms to take into account the individual's group when performing selection. But in cases of unintentional bias, this can mask problems. In the long term, it would be more beneficial to ensure groups have equal access to education than to upwardly adjust their scores. In fact, this upward bias to adjust for perceived unintentional lowered outcomes can in fact perpetuate the initial biases: the message is that a particular group underperforms, and thus needs extra help.