Explainable artificial intelligence in the talent recruitment process-a literature review

Abstract

With the widespread application of artificial intelligence in talent recruitment, the ‘black-box’ nature of AI, which leads to insufficient transparency and interpretability in decision-making, has gradually become a key challenge. This paper reviews the application of Explainable AI technologies throughout the entire talent recruitment process, aiming to analyze the role of XAI in enhancing decision-making transparency and traceability. The study finds that XAI, through interpretable algorithms such as LIME, SHAP, knowledge graphs, and causal reasoning, has significantly improved semantic understanding in resume parsing, precision in person-job recommendations, and analytical capabilities in interview evaluations. However, potential biases in generative large models, insufficient cross-scenario interpretability, and computational efficiency issues remain major bottlenecks. Future research should focus on dynamic fairness constraints and the integration of lightweight interpretability tools to promote the coordinated development of XAI in terms of ethical compliance, user trust, and practicality.