Applicant Perceptions of Hiring Algorithms - Uniqueness and Discrimination Experiences as Moderators

Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development

“…Reasons for the lower acceptance of algorithms in judging people and their potential are multifaceted. The usage of this new technology in HRM, combined with a lack of knowledge and transparency about how the algorithms work, increases emotional creepiness (e.g., Langer et al 2019;Langer and König 2018) and decreases interpersonal treatment and social interactions (e.g., Lee 2018) as well as fairness perceptions and the opportunity to perform (e.g., Kaibel et al 2019). To overcome these adverse impacts of algorithmic decision-making in HRM, companies need to promote their usage of algorithms (van Esch et al 2019) and make the processes more transparent of how algorithms are supporting the decisions of humans (Tambe et al 2019).…”

“…In general, algorithmic decision-making increases the standardization of procedures, so that decisions should be more objective and less biased, and errors should occur less frequently (Kaibel et al 2019), since information processing by human raters can be unsystematic, leading to contradictory and insufficient evidence-based decisions (Woods et al 2020). Consequently, procedural justice and distributive justice are higher using algorithmic decision-making, because the process is more standardized, which still not means that it is without bias.…”

“…Unlike the other studies, in an online experiment, Kaibel et al (2019) recently analyzed the perceived fairness of two different algorithmic decision tools, namely initial screening and digital interviews. Results show that algorithmic decisionmaking negatively affects personableness and the opportunity to perform during the selection process, but it does not affect the perceived consistency.…”

“…In general, knowledge about applicants' reactions when using algorithmic decision-making is still limited (van Esch et al 2019). Previous studies analyzed a single algorithmic decision tool [see Kaibel et al (2019) for a recent exception]. Consequently, there is a need to examine applicants' acceptance of algorithmic decision-making within the steps of the recruitment and selection process (e.g., media content and recruitment tools on the employer's webpage, recommender systems in social media, screening and preselection, telephone interview, and video interview).…”

“…Another question remains whether applicants and/or employees perceive the algorithmic decision-making to be fair. Previous studies showed that applicants' and employees' acceptance of algorithmic decision-making is lower in HR recruitment and HR development compared to common procedures conducted by humans (Kaibel et al 2019;Langer et al 2019;Lee 2018).…”