论文标题

在存在隐性偏见的情况下进行选择:交叉约束的优势

Selection in the Presence of Implicit Bias: The Advantage of Intersectional Constraints

论文作者

Mehrotra, Anay, Pradelski, Bary S. R., Vishnoi, Nisheeth K.

论文摘要

在招聘,晋升和大学录取等选择过程中,众所周知,候选人的种族,性别或性取向等社会质量属性的隐性偏见会造成持久的不平等,并减少决策者的总效用。已经提出了诸如鲁尼规则及其概括之类的干预措施,这些干预措施要求决策者至少选择每个受影响群体的指定数量的个体,以减轻隐性偏见在选择中的不利影响。最近的工作已经确定,在每个人最多属于一个受影响的群体的情况下,这种较低的约束对于改善总效用可能非常有效。但是,在某些情况下,个人可能属于多个受影响的群体,因此,由于这种交叉性,面对更极端的隐含偏见。我们考虑独立绘制的实用程序,并表明在相交的情况下,上述非交流约束只能在没有隐性偏见的情况下恢复可实现的总效用的一部分。另一方面,我们表明,如果一个人在交叉点上包含适当的下限约束,那么在没有隐式偏见的情况下,几乎所有实用程序都可以恢复。因此,相交的约束可以比减少尺寸的非相互作用方法可提供显着优势,以减少不平等。

In selection processes such as hiring, promotion, and college admissions, implicit bias toward socially-salient attributes such as race, gender, or sexual orientation of candidates is known to produce persistent inequality and reduce aggregate utility for the decision maker. Interventions such as the Rooney Rule and its generalizations, which require the decision maker to select at least a specified number of individuals from each affected group, have been proposed to mitigate the adverse effects of implicit bias in selection. Recent works have established that such lower-bound constraints can be very effective in improving aggregate utility in the case when each individual belongs to at most one affected group. However, in several settings, individuals may belong to multiple affected groups and, consequently, face more extreme implicit bias due to this intersectionality. We consider independently drawn utilities and show that, in the intersectional case, the aforementioned non-intersectional constraints can only recover part of the total utility achievable in the absence of implicit bias. On the other hand, we show that if one includes appropriate lower-bound constraints on the intersections, almost all the utility achievable in the absence of implicit bias can be recovered. Thus, intersectional constraints can offer a significant advantage over a reductionist dimension-by-dimension non-intersectional approach to reducing inequality.

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