In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to make talent-related decisions in a quantitative manner. Indeed, the recent development of Big Data and Artificial Intelligence (AI) techniques have revolutionized human resource management. The availability of large-scale talent and management-related data provides unparalleled opportunities for business leaders to comprehend organizational behaviors and gain tangible knowledge from a data science perspective, which in turn delivers intelligence for real-time decision-making and effective talent management at work for their organizations. In the last decade, talent analytics has emerged as a promising field in applied data science for human resource management, garnering significant attention from AI communities and inspiring numerous research efforts. To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of human resource management. Specifically, we first provide the background knowledge of talent analytics and categorize various pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant research efforts, categorized based on three distinct application-driven scenarios: talent management, organization management, and labor market analysis. In conclusion, we summarize the open challenges and potential prospects for future research directions in the domain of AI-driven talent analytics.

翻译：在当今竞争激烈且快速演变的商业环境中，组织亟需重新思考如何以量化方式做出人才相关决策。事实上，大数据与人工智能技术的近期发展已彻底改变了人力资源管理格局。大规模人才及管理数据的可用性为企业领导者提供了前所未有的机遇，使其能够理解组织行为并从数据科学视角获取切实洞见，进而为实时决策和工作中有效人才管理提供智能支持。过去十年间，人才分析作为应用数据科学在人力资源管理领域的新兴方向崭露头角，吸引了人工智能学界的广泛关注，并催生了大量研究。为此，我们针对人力资源管理领域中用于人才分析的人工智能技术，提供了最新且全面的综述。具体而言，我们首先阐述人才分析的背景知识，并对各类相关数据进行分类。随后，我们基于三种不同的应用驱动场景（人才管理、组织管理与劳动力市场分析）构建全面的研究分类框架。最后，我们总结了人工智能驱动型人才分析领域的现有挑战与未来研究方向的潜在前景。