Detecting Discrimination: A signal detection analysis

检测歧视：信号检测分析

• 批准号: 503990132
• 负责人: Professor Dr. Karl Christoph Klauer
• 金额: --
• 依托单位: Department of Psychology
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/503990132?language=en
• 关键词: Detecting; Discrimination; signal; detection; analysis

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?

歧视是基于种族、性别、年龄或性取向等特征对人和群体的不公平或偏见待遇。能够发现歧视是反对歧视的必要的第一步。然而，人们一致认为，支持或反对的证据。特定观察案例中的歧视通常是模糊的或隐藏的，因此，在不确定性的背景下对结果进行建模是信号检测理论（SDT）的目标，信号检测理论（SDT）一直是一个经典的理论框架。在研究中SDT 对理解这一判断过程最重要的贡献是分离和衡量促成这一过程的两个组成部分，即敏感性和反应偏差，即区分有或没有歧视线索的案例的能力。尽管SDT在相关文献的理论讨论中发挥了突出作用，但还没有实证研究可以对观察到的歧视归因进行SDT分析。本提案的目的是采取新范式来填补这一空白的第一步，SDT 分析有望做出两个贡献：（1）它们允许人们更准确地将经验发现描述为敏感性效应、响应偏差效应或两者的组合。 ，（2）他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培训的设计，以提高观察者和决策者的敏感度，以便在歧视案件发生时及时发现。 , 性别归因偏见、种族偏见及其交叉点将通过 SDT 分析进行调查，这使得人们能够解决基于 SDT 的理论有时已经提供了猜想的研究问题，但迄今为止还无法给出实证答案。下列问题：受歧视群体的成员与非歧视受害者甚至受歧视群体的成员的敏感性和/或反应是否不同？在此过程中，对典型歧视群体的陈规定型期望发挥了什么作用？特别是，情况如何这种期望的影响是通过对敏感性或反应偏差的影响来介导的吗？