OK computer: Worker perceptions of algorithmic recruitment
OK computer: Worker perceptions of algorithmic recruitment
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OK计算机：员工对算法招聘的看法
DOI：
10.1016/j.respol.2021.104420
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发表时间：
2022
期刊：
影响因子：
7.2
通讯作者：
A. Salomons
中科院分区：
文献类型：
--
作者：
E. Fumagalli;S. Rezaei;A. Salomons
我们通过两个激励实验提供了在线平台上的员工如何感知算法与人力招聘的证据，这些实验旨在激发员工为人力或算法评估付费的意愿。特别是，我们测试了员工表现的信息如何影响他们对招聘人员的选择，以及算法招聘人员是否被认为比人类招聘人员更有性别偏见。我们发现，尽管在我们的控制环境中，两名招聘人员都得到了相同的输入，但员工确实对人工评估和算法评估有不同的看法。具体来说，人类招聘人员被认为是更容易出错的评估者，更看重个人特征，而算法招聘人员则被认为更看重任务表现。与这些看法一致的是，任务绩效较好的员工更喜欢算法评估，而任务绩效较差的员工更喜欢人工评估。我们还发现了暗示性的证据，表明性别偏见的感知差异驱动了人类与算法招聘的偏好。
We provide evidence on how workers on an online platform perceive algorithmic versus human recruitment through two incentivized experiments designed to elicit willingness to pay for human or algorithmic evaluation. In particular, we test how information on workers’ performance affects their recruiter choice and whether the algorithmic recruiter is perceived as more or less gender-biased than the human one. We find that workers do perceive human and algorithmic evaluation differently, even though both recruiters are given the same inputs in our controlled setting. Specifically, human recruiters are perceived to be more error-prone evaluators and place more weight on personal characteristics, whereas algorithmic recruiters are seen as placing more weight on task performance. Consistent with these perceptions, workers with good task performance relative to others prefer algorithmic evaluation, whereas those with lower task performance prefer human evaluation. We also find suggestive evidence that perceived differences in gender bias drive preferences for human versus algorithmic recruitment.
影响算法决策中感知公平性的因素：算法结果、开发程序和个体差异
DOI：
10.1145/3313831.3376813
发表时间：
2020
期刊：
Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
影响因子：
--
作者：
Wang, Ruotong;Harper, F. Maxwell;Zhu, Haiyi
通讯作者：
Zhu, Haiyi