Lacroux Alain, Martin-Lacroux Christelle
Univ. Polytechnique Hauts de France, IDH, CRISS, Valenciennes, France.
Univ. Grenoble Alpes, Grenoble INP, CERAG, Grenoble, France.
Front Psychol. 2022 Jul 6;13:895997. doi: 10.3389/fpsyg.2022.895997. eCollection 2022.
Resume screening assisted by decision support systems that incorporate artificial intelligence is currently undergoing a strong development in many organizations, raising technical, managerial, legal, and ethical issues. The purpose of the present paper is to better understand the reactions of recruiters when they are offered algorithm-based recommendations during resume screening. Two polarized attitudes have been identified in the literature on users' reactions to algorithm-based recommendations: algorithm aversion, which reflects a general distrust and preference for human recommendations; and automation bias, which corresponds to an overconfidence in the decisions or recommendations made by algorithmic decision support systems (ADSS). Drawing on results obtained in the field of automated decision support areas, we make the general hypothesis that recruiters trust human experts more than ADSS, because they distrust algorithms for subjective decisions such as recruitment. An experiment on resume screening was conducted on a sample of professionals ( = 694) involved in the screening of job applications. They were asked to study a job offer, then evaluate two fictitious resumes in a 2 × 2 factorial design with manipulation of the type of recommendation (no recommendation/algorithmic recommendation/human expert recommendation) and of the consistency of the recommendations (consistent vs. inconsistent recommendation). Our results support the general hypothesis of preference for human recommendations: recruiters exhibit a higher level of trust toward human expert recommendations compared with algorithmic recommendations. However, we also found that recommendation's consistence has a differential and unexpected impact on decisions: in the presence of an inconsistent algorithmic recommendation, recruiters favored the unsuitable over the suitable resume. Our results also show that specific personality traits (extraversion, neuroticism, and self-confidence) are associated with a differential use of algorithmic recommendations. Implications for research and HR policies are finally discussed.
在许多组织中，由包含人工智能的决策支持系统辅助进行简历筛选目前正蓬勃发展，这引发了技术、管理、法律和伦理等方面的问题。本文的目的是更好地理解招聘人员在简历筛选过程中收到基于算法的推荐时的反应。在关于用户对基于算法的推荐的反应的文献中，已经确定了两种极端的态度：算法厌恶，它反映了对人类推荐的普遍不信任和偏好；以及自动化偏差，它对应于对算法决策支持系统（ADSS）做出的决策或推荐的过度自信。借鉴在自动决策支持领域获得的结果，我们提出一个总体假设，即招聘人员比ADSS更信任人类专家，因为他们不信任算法用于诸如招聘等主观决策。我们对694名参与求职申请筛选的专业人员样本进行了一项简历筛选实验。要求他们研究一份招聘启事，然后在一个2×2析因设计中评估两份虚拟简历，其中涉及推荐类型（无推荐/算法推荐/人类专家推荐）和推荐一致性（一致推荐与不一致推荐）的操作。我们的结果支持了对人类推荐偏好的总体假设：与算法推荐相比，招聘人员对人类专家推荐表现出更高水平的信任。然而，我们还发现推荐的一致性对决策有不同寻常的影响：在存在不一致的算法推荐的情况下，招聘人员更喜欢不合适的简历而不是合适的简历。我们的结果还表明，特定的人格特质（外向性、神经质和自信）与算法推荐的不同使用方式相关。最后讨论了对研究和人力资源政策的影响。
