Youth Trust and Ethical Perceptions of AI in HRM and Talent Analytics

Abstract

The purpose of this study is to examine the specific features of youth perceptions of artificial intelligence (AI) technologies in human resource management (HRM) and talent analytics, with a particular focus on the level of awareness, trust, and ethical aspects of algorithmic decision-making in recruitment processes. The study identifies the key factors influencing the formation of trust in AI and substantiates the conditions for its enhancement in the context of managerial and organizational maturity. Methods. The research employs a comprehensive approach combining bibliometric analysis of scientific publications (Scopus, VOSviewer), methods of systematization and generalization of academic sources, and an empirical survey. Data processing was carried out using descriptive statistics, index methods (Trust Index, Risk Index), Likert scales, and Pearson correlation analysis. In addition, content analysis of open-ended responses was applied to provide a qualitative interpretation of the findings. Results. The findings indicate that research on AI in HRM is rapidly expanding and inherently interdisciplinary, encompassing technological, managerial, and socio-ethical dimensions. Empirical evidence reveals that youth awareness of AI applications in recruitment remains largely fragmented, while neutral or cautious attitudes toward algorithmic decision-making prevail. The level of trust in AI is moderately low (TI ≈ 2.7), despite a high perceived functional effectiveness. A "trust paradox" has been identified, reflecting the coexistence of recognized efficiency of AI with a low willingness to rely on its decisions. Correlation analysis confirms statistically significant positive relationships between trust, perceived effectiveness, and objectivity of AI, as well as a negative relationship with perceived risks, including discrimination and algorithmic opacity. Explainability has been identified as the strongest determinant of trust. The proposed conceptual model reveals a gap between perceived awareness and actual experience of interaction with AI. Conclusions. The study demonstrates that the formation of trust in AI in HRM is a multidimensional process shaped by cognitive, functional, ethical, and behavioral factors. Key conditions for enhancing trust include ensuring algorithmic transparency, minimizing bias-related risks, and maintaining human involvement in decision-making (human-in-the-loop approach). The results have practical implications for improving HR strategies, advancing ethical standards for AI use, and developing hybrid recruitment models that balance technological efficiency with social responsibility.