PDF(Pubmed)
Abstract：
Extensive literature probes labor market discrimination through correspondence studies in which researchers send pairs of resumes to employers, which are closely matched except for social signals such as gender or ethnicity. Upon perceiving these signals, individuals quickly activate associated stereotypes. The Stereotype Content Model (SCM; Fiske 2002) categorizes these stereotypes into two dimensions: warmth and competence. Our research integrates findings from correspondence studies with theories of social psychology, asking: Can discrimination between social groups, measured through employer callback disparities, be predicted by warmth and competence perceptions of social signals? We collect callback rates from 21 published correspondence studies, varying for 592 social signals. On those social signals, we collected warmth and competence perceptions from an independent group of online raters. We found that social perception predicts callback disparities for studies varying race and gender, which are indirectly signaled by names on these resumes. Yet, for studies adjusting other categories like sexuality and disability, the influence of social perception on callbacks is inconsistent. For instance, a more favorable perception of signals like parenthood does not consistently lead to increased callbacks, underscoring the necessity for further research. Our research offers pivotal strategies to address labor market discrimination in practice. Leveraging the warmth and competence framework allows for the predictive identification of bias against specific groups without extensive correspondence studies. By distilling hiring discrimination into these two dimensions, we not only facilitate the development of decision support systems for hiring managers but also equip computer scientists with a foundational framework for debiasing Large Language Models and other methods that are increasingly employed in hiring processes.
摘要：
大量文献通过函授研究来探讨劳动力市场歧视，在函授研究中，研究人员将成对的简历发送给雇主，除了性别或种族等社会信号之外，它们是紧密匹配的。一旦感知到这些信号，个体迅速激活相关的刻板印象。刻板印象内容模型（SCM；Fiske2002）将这些刻板印象分为两个维度：温暖和能力。我们的研究将对应研究的结果与社会心理学理论相结合，问：可以区分社会群体，通过雇主回调差距来衡量，通过对社会信号的温暖和能力感知来预测？我们从21项出版的对应研究中收集回调率，592个社会信号不同。在这些社会信号上，我们从一组独立的在线评估者那里收集了温暖和能力感知。我们发现，社会感知可以预测种族和性别不同的研究的回调差异，这些简历上的名称间接发出信号。然而，用于调整其他类别的研究，如性和残疾，社会感知对回调的影响是不一致的。例如,对父母身份等信号的更有利的感知并不能始终如一地导致回调增加，强调了进一步研究的必要性。我们的研究提供了解决劳动力市场歧视的关键策略。利用温暖和能力框架，无需进行广泛的对应研究，即可对特定群体的偏见进行预测性识别。通过将招聘歧视提炼成这两个维度，我们不仅促进了招聘经理决策支持系统的开发，而且还为计算机科学家提供了一个基本框架，用于消除大型语言模型和其他在招聘过程中越来越采用的方法。
