OK computer: Worker perceptions of algorithmic recruitment

Elena Fumagalli, Sarah Rezaei and Anna Salomons

Research Policy, 2022, vol. 51, issue 2

Abstract: 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.

Keywords: Algorithmic evaluation; Technological change; Online labor market; Online experiment (search for similar items in EconPapers)
JEL-codes: C9 J24 M51 O33 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:respol:v:51:y:2022:i:2:s0048733321002146

DOI: 10.1016/j.respol.2021.104420

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