Perceived Quality of AI-Supported Recruitment: The Role of Explainability, Trust and Human Control

Abstract

Purpose: This paper examines perceived quality of Artificial Intelligence (AI)-supported recruitment by testing how explanation, confidence information, practical AI experience, trust, willingness to use AI, perceived fairness and human control shape evaluations of AI candidate-ranking recommendations. Methodology/Approach: The study uses an anonymous survey of Human Resources (HR)-related respondents (N = 145) and a randomised scenario comparing AI ranking without explanation with ranking supported by brief reasoning and confidence information. We use reliability checks, regression models, mediation and automation tests. Findings: Practical AI experience significantly moderated the effect of explanation on trust and showed a weaker pattern for willingness to use AI. The moderation was not significant for perceived fairness. Intention to use AI was associated with trust, perceived usefulness, human control and prior AI experience. Research Limitation/Implication: The non-probabilistic sample has a strong Central European component. The results measure perceptions rather than behavioural outcomes or audited fairness. Originality/Value of paper: The paper shows that explanation and confidence strengthen trust mainly among AI-experienced respondents but do not significantly improve perceived fairness. It distinguishes between trusting AI as decision support and being convinced that AI-supported ranking is fair to candidates.