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About this paper
Chuan Qin, Le Zhang, Yihang Cheng, Rui Zha, Dazhong Shen, Qi Zhang, Xi Chen, Ying Sun and 3 more
A comprehensive survey maps the landscape of Artificial Intelligence techniques applied to talent analytics, systematically categorizing data sources, methodologies, and applications across individual talent management, organizational dynamics, and broader labor market analysis. It consolidates existing research, highlights current capabilities, and outlines critical open challenges and future research directions for the field.
Key insights
- AI in talent analytics leverages a vast, diverse ecosystem of internal and external data, forming the foundation for holistic insights into talent, organizations, and the labor market.
- The field is increasingly dominated by advanced deep learning architectures and pre-trained language models (e.g., Transformers, BERT, LLMs), which are crucial for processing unstructured text, capturing complex relationships, and modeling dynamic patterns.
- Recurring challenges include data sparsity, bias, and a lack of public benchmark datasets, alongside pressing ethical concerns such as fairness, interpretability, and the need for dynamic modeling of human behavior and market trends.
Summary by alphaXiv.
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