Improving measurement and prediction in personnel selection through the application of machine learning
通过六个实际组织案例，展示机器学习在人员选拔中能像人类评委一样准确可靠地评分叙述信息，且更高效，同时能提高预测效度、减少负面影响，甚至能基于职位描述自动分析知识技能需求。
Abstract 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.