机器学习与人力资源管理:高效劳动力管理之路

Dr. Deepti Sharma, Dr. K. Sellvasundaram, Dr. Prasanta Chatterjee Biswas
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摘要

自机器智能(ML)增加人力资源管理(HRM)以来,人力资本工作的发展有好有坏。本文探讨了人力资源管理的财富、面临的问题以及在人工智能和ML时代的潜力。首先,我们讨论了数据处理的变化如何改变了人力资源管理(HRM),重点是人工智能和机器智能以何种方式或方法在变化的人力资源流程中变得更具影响力。本研究的目标是探究人类能力管理是什么、人工智能和人工智能如何影响人类能力管理、人工智能和人工智能将如何影响今后的任务,以及在人力资源管理中使用人工智能的利弊。这篇综述对人力资源管理的基本思想进行了深入细致的研究。它重点介绍了该领域如何从简单的政府任务转变为提高成员幸福感、产出和企业幸福感的重要举措。在这一部分,我们将进一步讨论人工智能和人工智能以何种方式或方法交换了人力资源任务,如引进、指导行动和规划受训员工。当人们研究人工智能和人工智能如何改变人力资源管理时,人们可以直观地看到,它们可能会提出家庭数据驱动的理解,使人力资源任务变得顺畅,并管理顺畅地处理操作员和创建决策。但是,要让机器智能在人力资源管理中发挥最大作用,还需要解决部分数据、不良数据和指导变化等问题。
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Machine Learning and HRM: A Path to Efficient Workforce Management
The developments in human capital work that have occurred since machine intelligence (ML) was increased human resource management (HRM) are both good and bad. This essay looks at what HRM wealth, what questions it faces, and what potential it offers in this place age of AI and ML. In the beginning, we discuss how changes in data processing have transformed human resource management (HRM), focusing on in what way or manner AI and machine intelligence are becoming more influential in changeful HR processes. The goals concerning this study search out research what human capability administration is, how AI and ML influence it, how AI and ML will influence tasks from now on, and the pros and cons of utilizing ML in HRM. The composition review investigates excellent detail about the fundamental ideas of human property administration. It focuses on how the field has exchanged over opportunity from simple governmental tasks to crucial exertions to better member happiness, output, and the happiness of the association. In this part, we further talk about in what way or manner AI and ML have exchanged HR tasks like bringing in, directing act, and planning the trained workers. When people examine how AI and ML have transformed HRM, people can visualize that they may present family data-driven understandings, make HR tasks smooth, and manage smooth to handle operators and create decisions. But to catch the most out of machine intelligence in HRM, issues like partial data, bad data, and directing change need expected fixed.
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