In recent years, both private and public organizations across contexts have begun implementing AI technologies in their recruitment processes. This transition is typically justified by improved efficiency as well as more objective, performance-based ranking, and inclusive selection of job candidates. However, this rapid development has also raised concerns that the use of these emerging technologies will instead increase discrimination or enhance the already existing inequality. In the present study, we first develop the concept of meta-algorithmic judgment to understand how recruiting managers may respond to automation of the hiring process. Second, we draw on this concept in the empirical assessment of the actual consequences of this type of transition by drawing on two large and unique datasets on employment records and job applications from one of Sweden's largest food retail companies. By comparing the outcomes of traditional and algorithmic job recruitment during this technological transition, we find that, contrary to the company's intentions, algorithmic recruitment decreases diversity. However, in contrast to what is often assumed, this is primarily not because the algorithms are biased, but because of what we identify as an unintended human–algorithmic interaction effect.

中文翻译：

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算法之后：对大型多站点公司招聘过程中的元算法判断和多样性的研究

近年来，不同背景下的私人和公共组织都开始在招聘流程中实施人工智能技术。这种转变通常是通过提高效率以及更加客观、基于绩效的排名以及对求职者的包容性选择来证明的。然而，这种快速发展也引发​​了人们的担忧，即这些新兴技术的使用反而会增加歧视或加剧已经存在的不平等。在本研究中，我们首先提出元算法判断的概念，以了解招聘经理如何应对招聘流程的自动化。其次，我们通过利用瑞典最大的食品零售公司之一的就业记录和工作申请的两个大型且独特的数据集，在对此类转型的实际后果进行实证评估时借鉴这一概念。通过比较这一技术转型期间传统招聘和算法招聘的结果，我们发现，与公司的意图相反，算法招聘降低了多样性。然而，与通常的假设相反，这主要不是因为算法有偏见，而是因为我们认为是无意的人机交互效应。