Detecting Discrimination: A signal detection analysis

歧视是基于种族、性别、年龄或性取向等特征对个人和群体的不公平或有偏见的待遇。能够发现歧视是打击歧视的必要的第一步，然而，人们一致认为，在所观察到的具体案件中，支持或反对歧视的证据往往是模糊不清或隐藏的。因此，将结果归因于歧视通常是在相当不确定的情况下发生的。信号检测理论（Signal Detection Theory，SDT）是研究辨别归因的经典理论框架，其目标是对不确定性下的决策进行建模。SDT对理解这一判断过程最重要的贡献是分离和测量了两个组成部分，敏感性和反应偏差。敏感性是指区分有和没有歧视线索的案件的能力。反应偏差是指相对于其他归因而言，倾向于将某一归因归因于歧视。尽管SDT在相关文献的理论讨论中发挥了重要作用，但还没有实证研究使SDT分析观察到的歧视归因成为可能。本建议的目的是迈出第一步，以新的模式填补这一空白。SDT分析承诺两个贡献：（1）它们允许人们更精确地将经验发现描述为敏感性效应，反应偏差效应或两者的组合，（2）它们通过澄清中介途径为解决这些发现的理论提供信息。从长远来看，由此产生的见解也有助于设计培训，使观察者和决策者敏感地发现歧视案件，当它们发生时，在该项目中，归因于性别偏见，种族偏见，以及它们的交叉点将通过特殊和差别待遇分析进行调查。这使人们能够解决一些研究问题，这些问题基于特殊和差别待遇的理论化有时已经提供了解释，但迄今为止还没有给出经验性的答案。例如：受歧视群体的成员与未受歧视或甚至受歧视优待的群体的成员在敏感性和/或反应方面是否有所不同？在这一过程中，对典型的歧视组合的陈规定型期望有何作用，特别是这种期望的影响如何通过对敏感性或反应偏差的影响来调节？

Discrimination is the unfair or prejudicial treatment of people and groups based on characteristics such as race, gender, age or sexual orientation. Being able to detect discrimination is a necessary first step in countering it. Yet, there is consensus that the evidence for or against discrimination in specific observed cases is often ambiguous or hidden. The attribution of an outcome to discrimination therefore regularly occurs in a context of considerable uncertainty. Modeling decisions under uncertainty is the goal of signal detection theory (SDT), that has been a classical theoretical framework in the study of attributions to discrimination. SDT’s most important contribution to understanding this judgmental process is to separate and measure two components that contribute to it, sensitivity and response bias. Sensitivity refers to the ability to distinguish between cases with and without cues to discrimination. Response bias refers to the tendency to prefer an attribution to discrimination relative to other attributions.Despite the prominent role that SDT has played in theoretical discussions in the relevant literature, there are no empirical studies that have made possible SDT analyses of observed attributions to discrimination. The purpose of the present proposal is to take a first step towards filling this gap with a new paradigm. SDT analyses promise two contributions: (1) They allow one to characterize empirical findings more precisely as sensitivity effects, response-bias effects, or combinations of both, and (2) they inform theories addressing these findings by clarifying mediational pathways. In the long term, the resulting insights can also contribute to the design of trainings to sensitize observers and decision makers to detect cases of discrimination when they occur.In the project, attributions to gender bias, racial bias, and their intersection are to be investigated by SDT analyses. This allows one to address research questions for which SDT-based theorizing has already sometimes offered conjectures, but for which empirical answers could so far not be given. Examples are the following: Do members of groups that are discriminated against differ in sensitivity and/or response from members of groups that are not victims of discrimination or are even favored by it? What is the role of stereotypical expectations about typical constellations of discrimination in this process and in particular, how are the effects of such expectations mediated – via effects on sensitivity or response bias?