Can I show my skills? Affective responses to artificial intelligence in the recruitment process

Abstract

Companies increasingly use artificial intelligence (AI) and algorithmic decision-making (ADM) for their recruitment and selection process for cost and efficiency reasons. However, there are concerns about the applicant’s affective response to AI systems in recruitment, and knowledge about the affective responses to the selection process is still limited, especially when AI supports different selection process stages (i.e., preselection, telephone interview, and video interview). Drawing on the affective response model, we propose that affective responses (i.e., opportunity to perform, emotional creepiness) mediate the relationships between an increasing AI-based selection process and organizational attractiveness. In particular, by using a scenario-based between-subject design with German employees (N = 160), we investigate whether and how AI-support during a complete recruitment process diminishes the opportunity to perform and increases emotional creepiness during the process. Moreover, we examine the influence of opportunity to perform and emotional creepiness on organizational attractiveness. We found that AI-support at later stages of the selection process (i.e., telephone and video interview) decreased the opportunity to perform and increased emotional creepiness. In turn, the opportunity to perform and emotional creepiness mediated the association of AI-support in telephone/video interviews on organizational attractiveness. However, we did not find negative affective responses to AI-support earlier stage of the selection process (i.e., during preselection). As we offer evidence for possible adverse reactions to the usage of AI in selection processes, this study provides important practical and theoretical implications.

Robots are judging me: Perceived fairness of algorithmic recruitment tools

“…While applicant reactions to algorithmic selection assessments, particularly game-based assessments, are generally positive ( Atkins et al, 2014 ; Lieberoth, 2015 ; Georgiou and Nikolaou, 2020 ; Leutner et al, 2020 ), the same cannot be said for fairness perceptions. At a general level, fairness perceptions are mixed and are not consistent between assessment tools ( Suen et al, 2019 ; Georgiou and Nikolaou, 2020 ; Köchling et al, 2022 ) but when looking at specific aspects of procedural justice, algorithmic tools are perceived as less fair than human ratings. Indeed, candidates perceive that there is less opportunity for behavioral control when assessments are automated compared to when they are judged by humans, meaning that they feel they are given less chance to perform and manipulate the raters to influence them toward a positive judgment ( Lee, 2018 ; Kaibel et al, 2019 ).…”

“…In contrast, Suen et al (2019) report that although there is a preference for synchronous video interviews compared to asynchronous, fairness perceptions do not vary when asynchronous video interviews are judged by humans compared to an algorithm. More recent research reveals further contrasts, with algorithmic tools being seen as less fair when used in later stages of the recruitment funnel and equally as fair as human ratings when used in earlier stages, such as resume screening ( Köchling et al, 2022 ).…”

“…Indeed, despite acknowledging that algorithmic recruitment tools are more objective than human ratings, and that human ratings can be biased, participants still report perceiving algorithmic recruitment tools are less fair due to the lack of human connection and interaction ( Mirowska and Mesnet, 2021 ). This could explain why algorithmic tools used earlier in the funnel, where there is typically less human interaction, are seen as equally fair to human ratings, while algorithmic tools used later in the funnel, such as during the interview stage, are viewed as less fair than human ratings ( Köchling et al, 2022 ) since there are differences in the level of human connection expected.…”

Better explaining the benefits why AI? Analyzing the impact of explaining the benefits of AI‐supported selection on applicant responses

“…Several different rules of procedural justice contribute to an individual's assessment of fairness (Cropanzano et al, 2015; Leventhal, 1980), especially during selection processes (Gilliland, 1993; Köchling et al, 2022; Langer, König & Fitili, 2018). Besides others, the list of the procedural rules consists of consistency, bias‐suppression, ethicality, and accuracy (Cropanzano et al, 2015; Leventhal, 1980).…”

“…Previous research showed that applicant reactions to AI‐supported selection tools are predominantly negative in terms of justice perceptions (e.g., Acikgoz et al, 2020; Köchling et al, 2022; Langer & Landers, 2021; Langer et al, 2020; Newman et al, 2020; Wesche & Sonderegger, 2021). While taking advantage of AI‐supported selection tools and not discouraging applicants and keeping them in the selection process at the same time, it is paramount to examine the possible actions that organizations can take to improve applicant reactions in personnel selection.…”