ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN RECRUITING AND STAFF MANAGEMENT SYSTEMS
Abstract
The article examines modern approaches to the use of artificial intelligence (AI) in recruitment and staff development processes under the conditions of business digital transformation. Theoretical foundations and practical tools for the automation of HR processes are analyzed, focusing on the integration of intelligent technologies into recruitment, evaluation, and employee retention systems. Special attention is paid to augmented writing technologies for generating job descriptions that enhance linguistic relevance and gender neutrality, as well as to automated candidate profiling in social networks, intelligent resume scanners, chatbots, and AI-based preliminary testing systems. The study provides a detailed analysis of machine learn- ing and natural language processing algorithms, including BERT, GPT, and T5 models, which enable semantic analysis and classification of candidate profiles, as well as predictive models for assessing employee success. The typical stages of data processing are examined, including the creation of embeddings (vector representations) of candidate profiles and the use of vector databases for semantic matching, particularly Milvus, Pinecone, and Weaviate, which ensure context-aware candidate search and ranking. Mechanisms for integrating such models into automated recruitment systems, performance evaluation tools, and employee success prediction frameworks are discussed. The paper analyzes performance metrics such as accuracy, precision, recall, and F1-score, used for quantitative measurement of HR analytics effectiveness. It is demonstrated that the integration of intelligent technologies can reduce recruitment time by 40–60%, increase selection accuracy by 25–30%, and lower bias in decision-making processes. Ethical challenges associated with AI use are also outlined, including algorithmic discrimination, model opacity, data privacy risks, and data access limitations. The study proposes approaches to ensuring transparency and fairness of AI-driven systems through Explainable AI methods and compliance with GDPR principles. The findings have practical significance for HR technology developers, analysts, and resource managers implementing intelligent automation systems in recruitment and staff management processes.
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