Rejected by an AI? Comparing job applicants’ fairness perceptions of artificial intelligence and humans in personnel selection - Malin, Christine; Fleiß, Jürgen; Ortlieb, Renate; Thalmann, Stefan (Frontiers in Artificial Intelligence, 2025)
IntroductionArtificial intelligence (AI) transforms personnel selection, but the application of AI raises fairness concerns and aversion towards AI. Although job applicants may perceive the selection process as fairer when they receive an explanation for the decision, scientific knowledge about AI-related fairness perceptions in this setting is limited. This paper investigates how job applicants perceive fairness of an AI-based personnel selection process considering explanations provided.MethodsThe hypotheses are based on a theoretical framework about fairness and literature on algorithm aversion. Data were collected through a vignette-style method focusing on four personnel selection scenarios (n = 921).ResultsWe show that provided explanations increase job applicants’ perceptions of outcome fairness, process fairness, interpersonal treatment, and recommendation intention, irrespective of the decision being made by an AI or human.DiscussionWe provide conclusions for algorithmic decision-making and discuss factors that need to be considered when adopting and designing AI so that AI is perceived as fair.