Job applicants' attitudes and emotions towards the use of artificial intelligence in recruitment

Abstract

Artificial intelligence (AI) is now embedded across recruitment, from attraction and pre-screening to applicant communication and early assessments. Candidates, however, judge hiring not only by efficiency but also by how transparent, fair, and human the process feels. This thesis examined job applicants’ attitudes and emotions toward AI-mediated recruitment and the factors that shape these perceptions. The theoretical framework integrates the digitalization of recruitment, Human-AI Interaction, candidate experience, and the literatures on trust and fairness in AI.

A quantitative online survey was conducted. The survey concluded 23 items: 21 closed questions using a five-point Likert scale and two optional open-ended questions. Background variables included age, current employment status, and whether the respondent had applied for a job in the past 12 months. The survey was distributed digitally, remained open for three weeks, and participation was voluntary and anonymous. The survey was designed to be concise and was pilot-tested prior to launch. Data were analysed in Google Sheets using percentage distributions.

Findings show high awareness of AI’s growing role in hiring and cautiously positive views of efficiency gains. At the same time, respondents widely perceived AI-led processes as impersonal and reported discomfort when machines, rather than people, make consequential decisions. Human contact and oversight were preferred, especially in later stages. Perceived transparency was low and trust in equal treatment limited, though some believed AI can reduce human bias. Emotional responses were mixed: anxiety and stress increased for some when AI evaluated applications, yet no unanimous pattern emerged across all measures. Open-ended responses emphasised clear disclosure of AI’s role, understandable reasons for automated outcomes, robust data-protection practices, and the option to request human review.

In conclusion, AI should augment, and not replace human judgment in recruitment. Recommended improvements include explicit disclosure of where and how AI is used, brief human-readable rationales for automated results, accessible routes to human evaluation, and systematic bias monitoring. Reliability was supported by a standardised instrument and pilot testing; generalisability is limited by a convenience sample skewed toward younger adults and potential self-selection bias. Future research should include qualitative interviews, cross-industry and demographic comparisons, longitudinal tracking as regulation matures, and experiments testing whether added transparency and human-AI collaboration measurably improve trust and candidate experience.