THE ROLE OF ARTIFICIAL INTELLIGENCE IN MODERN RECRUITMENT AND SELECTION PROCESS
Keywords:
AI Powered recruitment tools, Job Applicants perception, Efficiency, AI tool design.Abstract
Rapid technological development has led to research focused on combining recruitment and information technology (IT). Typically, the focus has been on how to make the recruitment and selection process smoother and optimized using IT or on technological
advances that offer a new, smart, digital context for human resource management (HRM) practices. Moreover, time, effort, and repeating daily tasks are transformed into computer driven ones, which gives recruiters enough room to focus on more important issues related to performance improvement and development. AI algorithms are only as good as the data on which they are trained, and if that data is biased, so are the algorithms (Danks & London, 2017). This study explores job applicants' perceptions of AI-powered recruitment tools, focusing on factors such as trust, fairness, transparency, and effectiveness. A descriptive research design was employed to examine these perceptions comprehensively. Convenience sampling was used to select a sample of 181 respondents. Data analysis techniques included ANOVA and regression analysis to uncover insights into applicants' attitudes toward AI-driven recruitment systems. The findings reveal that applicants generally hold positive perceptions of AI technology in hiring processes, recognizing its potential to enhance the efficiency of recruitment systems when integrated with human involvement. These results underscore the importance of designing AI tools
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References
Rigotti, C., & Fosch‐Villaronga, E. (2024). Fairness, AI & recruitment. Computer Law and Security Report/Computer Law & Security Report, 53, 105966. https://doi.org/10.1016/j.clsr.2024.105966
Ramesh, S., & Das, S. (2022). Adoption of AI in Talent Acquisition: A Conceptual framework. In Lecture notes in networks and systems (pp. 12–20). https://doi.org/10.1007/978-3-031-01942-5_2
Lee, C., & Cha, K. (2023). FAT-CAT—Explainability and augmentation for an AI system: A case study on AI recruitment-system adoption. International Journal of Human-computer Studies, 171, 102976. https://doi.org/10.1016/j.ijhcs.2022.102976
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