Selection Problems in the Presence of Implicit Bias
Selection Problems in the Presence of Implicit Bias
复制标题
存在隐性偏见时的选择问题
DOI：
10.4230/lipics.itcs.2018.33
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Manish Raghavan
中科院分区：
文献类型：
--
作者：
J. Kleinberg;Manish Raghavan
在过去的二十年中，内隐偏见的概念已成为我们理解招聘、晋升和学校招生等活动中歧视现象的一个重要组成部分。关于内隐偏见的研究认为，当人们评估他人时——例如在招聘情境中——他们对特定群体成员身份的无意识偏见会影响其决策，即使他们无意歧视这些群体的成员。越来越多的实验研究表明了内隐偏见在产生不良结果方面的影响。
在此，我们提出一个用于研究内隐偏见对选拔决策影响的理论模型，以及一种在该模型内分析针对内隐偏见可能的程序补救措施的方法。我们的模型所代表的一个典型情况是招聘场景：一个招聘委员会试图从求职者中选出一组进入面试的入围者，根据他们的未来潜力对这些求职者进行评估，但由于对某一群体成员存在内隐偏见，他们对潜力的评估出现了偏差。在这个模型中，我们表明诸如“鲁尼规则”（要求至少有一名入围者从受影响的群体中选出）这样的措施不仅可以提高该受影响群体的代表性，还能为进行招聘的组织带来更高的绝对收益。然而，确定这些措施在何种条件下能够带来更高收益，涉及到偏见程度与求职者特征的潜在分布之间的微妙权衡，从而引出了在存在概率辅助信息的情况下有关顺序统计的新理论问题。
Over the past two decades, the notion of implicit bias has come to serve as an important component in our understanding of discrimination in activities such as hiring, promotion, and school admissions. Research on implicit bias posits that when people evaluate others -- for example, in a hiring context -- their unconscious biases about membership in particular groups can have an effect on their decision-making, even when they have no deliberate intention to discriminate against members of these groups. A growing body of experimental work has pointed to the effect that implicit bias can have in producing adverse outcomes.
Here we propose a theoretical model for studying the effects of implicit bias on selection decisions, and a way of analyzing possible procedural remedies for implicit bias within this model. A canonical situation represented by our model is a hiring setting: a recruiting committee is trying to choose a set of finalists to interview among the applicants for a job, evaluating these applicants based on their future potential, but their estimates of potential are skewed by implicit bias against members of one group. In this model, we show that measures such as the Rooney Rule, a requirement that at least one of the finalists be chosen from the affected group, can not only improve the representation of this affected group, but also lead to higher payoffs in absolute terms for the organization performing the recruiting. However, identifying the conditions under which such measures can lead to improved payoffs involves subtle trade-offs between the extent of the bias and the underlying distribution of applicant characteristics, leading to novel theoretical questions about order statistics in the presence of probabilistic side information.