[论文解读] Selection Problems in the Presence of Implicit Bias

Jon Kleinberg, Manish Raghavan|arXiv (Cornell University)|Jan 4, 2018

Names, Identity, and Discrimination ResearchSocial Sciences被引用 21

一句话总结

本文提出一个理论模型，分析隐性偏见如何影响招聘中的选拔决策，特别是评估罗尼规则（即要求至少一名代表性不足群体的候选人接受面试）的影响。研究发现，在特定条件（包括偏见强度、少数群体代表性以及候选人质量分布）下，罗尼规则可通过纠正偏差评估，同时提升多样性与组织收益。

ABSTRACT

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.

研究动机与目标

• 将隐性偏见对招聘及其他类似过程中选拔决策的影响形式化。
• 分析程序性补救措施（如罗尼规则）是否能同时改善代表性与组织成果。
• 确定罗尼规则在何种条件下可提升被选拔候选人的期望质量，即使存在偏见。
• 探讨偏见强度、少数群体代表性与候选人质量分布之间在选拔结果中的相互作用。
• 推导罗尼规则在选拔过程中同时提升公平性与绩效的理论条件。

提出的方法

• 建模一个招聘场景，其中评估者对特定群体存在隐性偏见，从而扭曲其对候选人潜力的评估。
• 使用独立同分布随机变量的顺序统计量来建模候选人评估的分布，其中一组候选人受到偏见影响。
• 引入一个涉及三个参数的正式条件：偏见强度、少数群体普遍性以及候选人质量的阈值。
• 应用顺序统计量的概率界（例如，Pr[X_{(n-1:n)} ≥ T ∩ Y_{(n-1:n)} ≥ T] ≤ 2nF(T)^{n-1}）以分析高绩效少数群体候选人被忽略的可能性。
• 通过条件期望分析，比较有无罗尼规则时被选拔候选人的期望质量。
• 利用高绩效候选人截断分布模型，推导罗尼规则下候选人质量期望提升的下界。

实验结果

研究问题

• RQ1 在存在隐性偏见的情况下，罗尼规则在何种条件下可提升被选拔候选人的期望质量？
• RQ2 偏见强度、少数群体代表性与候选人质量分布之间的相互作用如何影响罗尼规则的有效性？
• RQ3 当多数群体候选人整体更强时，罗尼规则是否仍可通过偏见引发的错误，带来更高的组织收益？
• RQ4 概率性辅助信息（如质量阈值）在决定保有多样性的选拔规则成败中起什么作用？
• RQ5 是否存在一个偏见强度与代表性之间的临界阈值，使得罗尼规则从有益转变为有害？

主要发现

• 当对少数群体的偏见足够强烈且合格少数群体候选人的数量足够大时，罗尼规则可提升被选拔候选人的期望质量。
• 存在一个临界阈值，只有当偏见强度与少数群体普遍性的乘积超过与候选人质量分布相关的临界值时，罗尼规则才能改善结果。
• 罗尼规则下候选人质量的期望提升存在一个下界，其形式为与(F(T) + η)^{n-1}成比例的函数，其中F(T)为质量在阈值T处的累积分布函数。
• 模型表明，即使多数群体候选人平均质量更强，罗尼规则仍可纠正由偏见引发的错误，带来更优的整体选拔结果。
• 分析揭示，罗尼规则的有效性不仅取决于偏见和代表性，还取决于潜在质量分布的形状，特别是尾部分布的行为特征。
• 对顺序统计量的理论界（例如，Pr[X_{(n-1:n)} ≥ T ∩ Y_{(n-1:n)} ≥ T] ≤ 2nF(T)^{n-1}）对于量化高绩效少数群体候选人被忽略的风险至关重要。