Robots are judging me: Perceived fairness of algorithmic recruitment tools

Hilliard, Airlie; Guenole, Nigel and Leutner, Franziska. 2022. Robots are judging me: Perceived fairness of algorithmic recruitment tools. Frontiers in Psychology, 13, 940456. ISSN 1664-1078 [Article]

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Abstract or Description

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.

Item Type:


Identification Number (DOI):


selection, recruitment, fairness, perceptions, psychometrics, algorithm, machine learning

Departments, Centres and Research Units:

Institute of Management Studies


5 July 2022Accepted
25 July 2022Published

Item ID:


Date Deposited:

14 Feb 2023 11:45

Last Modified:

14 Feb 2023 11:50

Peer Reviewed:

Yes, this version has been peer-reviewed.


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