A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics

• URL: http://arxiv.org/abs/2307.03195v2
• Date: Mon, 6 May 2024 03:18:44 GMT
• Title: A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics
• Authors: Chuan Qin, Le Zhang, Yihang Cheng, Rui Zha, Dazhong Shen, Qi Zhang, Xi Chen, Ying Sun, Chen Zhu, Hengshu Zhu, Hui Xiong,
• Abstract summary: Talent analytics has emerged as a promising field in applied data science for human resource management. Recent development of Big Data and Artificial Intelligence techniques have revolutionized human resource management.
• Score: 46.025337523478825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• The Transformative Impact of AI and Deep Learning in Business: A Literature Review [0.0]
  This paper aims to review the radical role of AI and deep learning in various functional areas of the business. It covers material applications in the healthcare sector, the retail and manufacturing industry, agriculture and farming, and finance.
  arXiv  Detail & Related papers  (2024-10-30T20:35:03Z)
• Evaluation of OpenAI o1: Opportunities and Challenges of AGI [112.0812059747033]
  o1-preview demonstrated remarkable capabilities, often achieving human-level or superior performance. The model excelled in tasks requiring intricate reasoning and knowledge integration across various fields. Overall results indicate significant progress towards artificial general intelligence.
  arXiv  Detail & Related papers  (2024-09-27T06:57:00Z)
• Data Analysis in the Era of Generative AI [56.44807642944589]
  This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
  arXiv  Detail & Related papers  (2024-09-27T06:31:03Z)
• Fairness in AI-Driven Recruitment: Challenges, Metrics, Methods, and Future Directions [0.0]
  Big data and machine learning has led to a rapid transformation in the traditional recruitment process. Given the prevalence of AI-based recruitment, there is growing concern that human biases may carry over to decisions made by these systems. This paper provides a comprehensive overview of this emerging field by discussing the types of biases encountered in AI-driven recruitment.
  arXiv  Detail & Related papers  (2024-05-30T05:25:14Z)
• On the Opportunities of Green Computing: A Survey [80.21955522431168]
  Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades. The needs for high computing power brings higher carbon emission and undermines research fairness. To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
  arXiv  Detail & Related papers  (2023-11-01T11:16:41Z)
• AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges [60.56413461109281]
  Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes. We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful. We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions.
  arXiv  Detail & Related papers  (2023-04-10T15:38:12Z)
• Towards AI-Empowered Crowdsourcing [27.0404686687184]
  We propose a taxonomy which divides AI-Empowered Crowdsourcing into three major areas: task delegation, motivating workers, and quality control. We discuss the limitations and insights, and curate the challenges of doing research in each of these areas to highlight promising future research directions.
  arXiv  Detail & Related papers  (2022-12-28T05:06:55Z)
• Designing an AI-Driven Talent Intelligence Solution: Exploring Big Data to extend the TOE Framework [0.0]
  This study aims to identify the new requirements for developing AI-oriented artifacts to address talent management issues. A design science method is adopted for conducting the experimental study with structured machine learning techniques.
  arXiv  Detail & Related papers  (2022-07-25T10:42:50Z)
• Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper [50.25428141435537]
  Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between machine learning, big data, streaming analytics, and the management of IT operations. Main aim of the AIOPS workshop is to bring together researchers from both academia and industry to present their experiences, results, and work in progress in this field.
  arXiv  Detail & Related papers  (2021-01-15T10:43:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.