When Algorithms Screen Applicants: AI Hiring, Procedural Justice, and Early-Career Employment Opportunities

Artificial intelligence (AI) is increasingly used in recruitment, but studies of applicants' perceptions, technical fairness, and employment outcomes answer different questions. This structured integrative literature review examines AI-assisted hiring in relation to procedural justice and early-career employment opportunity. Exploratory searches of Academic Search Complete through Liberty University's EBSCO platform took place between June and October 2026, supplemented by targeted source identification. The synthesis draws on a working inventory of 28 references comprising empirical studies, technical and simulation work, reviews, and methodological or governance scholarship. Experimental and survey evidence links applicants' judgments to transparency, human involvement, explanations, social presence, and respectful communication, although findings vary by design and context. Technical and simulation studies illustrate the importance of selection criteria and pipeline-level auditing but do not establish effects on actual hiring. Evidence directly tracking early-career applicants from screening through interviews, offers, and employment is limited in the assembled literature. The review proposes a three-part analytical distinction between perceived procedural justice, assessment validity and auditability, and observed recruitment progression. Because search and appraisal documentation remain incomplete, the findings are interpretive rather than exhaustive or causal.