使用机器学习进行招聘过程中的绩效预测和绩效评估

Ali A. Mahmoud, Tahani AL Shawabkeh, W. Salameh, Ibrahim I. Al Amro
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引用次数: 20

摘要

随着第四次工业革命的开始,在当今竞争激烈的行业中,寻找合适的、合格的、聪明的和有潜力的人才来填补空缺的需要,雇主们正在将招聘过程带入数字世界。由于人工智能(AI)具有高速计算能力和对大数据的适应性,因此它习惯于以一种简单的方式对数据进行分析并表示给雇主,以便他们有效地做出决策。一个即将到来的挑战是,一个空缺的新候选人是否会根据招聘标准给出预期的表现,以及如何在处理招聘过程中雇佣一个这样的候选人?雇主们关心的是对现有员工的绩效评估,但在招聘之前了解新候选人的表现是一项挑战。本研究提出了一个后续的概念模型,即在招聘过程中使用人工智能(AI),通过分析员工的历史表现和条件,使用绩效管理和社会筛选来预测新候选人的预期绩效。这种方法将提供一个额外的参数,帮助决策者在招聘过程中。虽然这种方法是消除不良招聘的一个进步,但它需要大量的历史数据,包括绩效跟踪,从调查和社交媒体等多个来源收集的个人信息以及与老员工和现任员工时间相关的员工状况,才能给出更高效和准确的结果。
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Performance Predicting in Hiring Process and Performance Appraisals Using Machine Learning
In the nowadays-competitive race of finding a suitable talented, qualified, bright and potential personnel to fulfill the needed spot of a vacancy in an industry, and with the beginning of the fourth industrial revolution, employers are taking the hiring process to the digital world. As Artificial Intelligent (AI) has a high-speed computation and adaptively to big data, it used to analyses and represents the data to employers in an easy way so they can make their decisions effectively.An upcoming challenge is raised where if the new candidates for a vacancy will give the expected performance based on the hiring criteria's or not, and how to hire a candidate that will while dealing with the hiring process? Employers are concerned with the performance evaluation of their current employees, but it is a challenge knowing the performance of new candidates before hiring.This study is proposing a follow-up conceptual model of using Artificial Intelligent (AI) in the hiring process with the using of performance management and social screening to predict the new candidate expected performance by analyzing historical performances and conditions of employees. This method will give an additional parameter that assists the decision makers in the hiring process.Although this method is a step forward to eliminate bad hiring, but it is requiring a huge historical data including the tracking of the performance, personal information collected from several sources like surveys and social Media and employees conditions related to the time of old and current employees, to give results that are more efficient and accurate.
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