Interventions for Ranking in the Presence of Implicit Bias

Celis, L. Elisa, Mehrotra, Anay, Vishnoi, Nisheeth K.

arXiv.org Artificial Intelligence 

It is well understood that implicit bias is a factor in adverse effects against subpopulations in many societal contexts [1,6,42] as also highlighted by recent events in the popular press [22,38,61]. For instance, in employment decisions, men are perceived as more competent and given a higher starting salary even when qualifications are the same [52], and in managerial jobs, it was observed that women had to show roughly twice as much evidence of competence as men to be seen as equally competent [37,59]. In education, implicit biases have been shown to exist in ways that exacerbate the achievement gap for racial and ethnic minorities [53] and female students [41], and add to the large racial disparities in school discipline which particularly affect black students' school performance and future prospects [45]. Beyond negatively impacting social opportunities, implicit biases have been shown to put lives at stake as they are a factor in police decisions to shoot, negatively impacting people who are black [20] and of other racial or ethnic minorities [48]. Furthermore, decision making that relies on biased measures of quantities such as utility can not only adversely impact those perceived more negatively, but can also lead to sub-optimal outcomes for those harboring these unconscious biases. To combat this, a significant effort has been placed in developing anti-bias training with the goal of eliminating or reducing implicit biases [24, 39, 64].

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