Abstract & Overview
Artificial intelligence is revolutionising employee selection, helping firms scan applications, rank prospects and make faster hiring decisions. Nevertheless, the application of AI in recruitment also raises questions about algorithmic bias, transparency, regulatory compliance and the ongoing relevance of human judgment. Automated hiring has been studied in terms of its technical and ethical implications, but little is known about how recruiters really work with human-AI partnership. The objective of this study was to explore how U.S. business recruiters incorporate AI recommendations with human judgement and to understand the elements that influence their trust, acceptance and use of AI-supported hiring tools. A phenomenological qualitative design was employed Primary data was acquired through semi-structured interviews with 25 HR professionals working in technology, finance, healthcare and education businesses, identified using purposive and snowball sampling. The study is based on the technology acceptance model, human-automation interaction theory and socio-technical systems theory. Thematic analysis of interview transcripts was conducted using iterative coding and NVivo, assisted by member verification and peer debriefing. Three themes emerged: AI enhanced screening speed but required human verification; algorithmic fairness, transparency, and legal compliance influenced recruiter trust; and human–AI partnership transitioned recruiters to strategic and supervision positions. This implies that AI-enabled recruitment is seen as a decision assistant rather than a replacement of recruiters. The report suggests training for recruiters, explainable AI, bias audits, and human-in-the-loop governance. It is cross-sectional, self-reported, and U.S.-focused which restricts its generalizability. Future research should investigate the long-term implications on quality of selection, workforce diversity, and candidate experience.