ARTIFICIAL INTELLIGENCE IN CANDIDATE SCREENING: OPPORTUNITIES AND CHALLENGES

Abstract

Purpose: This paper explores how artificial intelligence (AI) supports candidate screening and identifies key challenges organizations face when adopting AI-based recruitment tools. The aim is to provide a critical synthesis of recent peer-reviewed research and conference literature to inform theory and practice. Design/methodology/approach: The study is based on a structured review of selected academic articles and conference papers published in recent years. It uses thematic analysis to evaluate the benefits and limitations of AI in recruitment, focusing specifically on the screening phase. The paper adopts a socio-technical lens, addressing technological, organizational, ethical, and legal aspects of AI implementation. Findings: AI significantly enhances candidate screening by automating repetitive tasks, increasing efficiency, and enabling data-driven, objective assessments. Key benefits include improved scalability, predictive accuracy, and reduced bias—when implemented carefully. However, challenges persist. These include algorithmic bias, opacity in decision-making, data privacy concerns, regulatory uncertainty, and resistance from HR professionals and candidates. The study underscores the need for transparent AI systems, ethical design principles, and human oversight to ensure trust and fairness. Research limitations/implications: The findings offer guidance for HR practitioners and AI developers. Organizations should foster interdisciplinary collaboration, invest in training, and ensure algorithmic transparency. Responsible implementation and continuous evaluation are key to maximizing benefits while minimizing risks. Practical implications: This study provides actionable insights for HR practitioners and technology developers. It emphasizes the importance of interdisciplinary collaboration, responsible algorithm design, and transparency to ensure successful and ethical AI integration. Organizations are advised to invest in data literacy, cross-functional AI governance, and continuous system evaluation to maximize benefits while mitigating risks. Originality/value: This paper contributes to the literature on AI in human resource management by synthesizing recent findings into a framework of benefits and barriers in candidate screening. It provides theoretical and practical insights for researchers, practitioners, and developers working at the intersection of HR and AI.