Machine learning (ML) is being widely adopted by organizations to assist in selecting personnel, commonly by scoring narrative information or by eliminating the inefficiencies of human scoring. This combined article presents six such efforts from operational selection systems in actual organizations. The findings show that ML can score narrative information collected from candidates either in writing or orally in response to assessment questions (called constructed response) as accurately and reliably as human judges, but much more efficiently, making such responses more feasible to include in personnel selection and often improving validity with little or no adverse impact. Moreover, algorithms can generalize across assessment questions, and algorithms can be created to predict multiple outcomes simultaneously (e.g., productivity and turnover). ML has even been demonstrated to make job analysis more efficient by determining knowledge and skill requirements based on job descriptions. Collectively, the studies in this article illustrate the likely major impact that ML will have on the practice and science of personnel selection from this point forward.

中文翻译：

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通过机器学习的应用改进人员选拔的测量和预测

机器学习 (ML) 被组织广泛采用来协助选择人员，通常是通过对叙述信息进行评分或消除人工评分的低效率。这篇综合文章介绍了实际组织中运营选择系统的六项此类努力。研究结果表明，机器学习可以像人类法官一样准确可靠地对从候选人那里收集的书面或口头叙述信息进行评分，以回答评估问题（称为构建响应），但效率更高，从而使此类回答更容易纳入人员选拔中并且通常会提高有效性，而几乎没有或没有负面影响。此外，算法可以概括评估问题，并且可以创建算法来同时预测多个结果（例如，生产率和营业额）。机器学习甚至被证明可以根据职位描述确定知识和技能要求，从而提高职位分析的效率。总的来说，本文中的研究说明了从现在开始机器学习可能对人员选拔的实践和科学产生的重大影响。