ReviewFrontiers in psychology2022
Robots are judging me: Perceived fairness of algorithmic recruitment tools.
Review in Frontiers in psychology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 18 citations in OpenAlex.
- Perceived AI interview design and organizational attractiveness: the roles of social presence, telepresence, and AI literacy.Frontiers in psychology · 2026Article
- Rejected by an AI? Comparing job applicants' fairness perceptions of artificial intelligence and humans in personnel selection.Frontiers in artificial intelligence · 2025Article
- Check the box! How to deal with automation bias in AI-based personnel selection.Frontiers in psychology · 2023Article
- Gender equity in hiring: examining the effectiveness of a personality-based algorithm.Frontiers in psychology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent years have seen rapid advancements in selection assessments, shifting away from human and toward algorithmic judgments of candidates. Indeed, algorithmic recruitment tools have been created to screen candidates' resumes, assess psychometric characteristics through game-based assessments, and judge asynchronous video interviews, among other applications. While research into candidate reactions to these technologies is still in its infancy, early research in this regard has explored user experiences and fairness perceptions. In this article, we review applicants' perceptions of the procedural fairness of algorithmic recruitment tools based on key findings from seven key studies, sampling over 1,300 participants between them. We focus on the sub-facets of behavioral control, the extent to which individuals feel their behavior can influence an outcome, and social presence, whether there is the perceived opportunity for a social connection and empathy. While perceptions of overall procedural fairness are mixed, we find that fairness perceptions concerning behavioral control and social presence are mostly negative. Participants feel less confident that they are able to influence the outcome of algorithmic assessments compared to human assessments because they are more objective and less susceptible to manipulation. Participants also feel that the human element is lost when these tools are used since there is a lack of perceived empathy and interpersonal warmth. Since this field of research is relatively under-explored, we end by proposing a research agenda, recommending that future studies could examine the role of individual differences, demographics, and neurodiversity in influencing fairness perceptions of algorithmic recruitment.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.