Organisations are increasingly relying on people analytics to aid human resources decision-making. One application involves using machine learning to automatically infer applicant characteristics from employment interview responses. However, management research has provided scant validity evidence to guide organisations' decisions about whether and how best to implement these algorithmic approaches. To address this gap, we use closed vocabulary text mining on mock video interviews to train and test machine learning algorithms for predicting interviewee's self-reported (automatic personality recognition) and interviewer-rated personality traits (automatic personality perception). We use 10-fold cross-validation to test the algorithms' accuracy for predicting Big Five personality traits across both rating sources. The cross-validated accuracy for predicting self-reports was lower than large-scale investigations using language in social media posts as predictors. The cross-validated accuracy for predicting interviewer ratings of personality was more than double that found for predicting self-reports. We discuss implications for future research and practice.

中文翻译：

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开发和评估基于语言的机器学习算法，用于在视频面试中推断申请人的性格

组织越来越依赖人员分析来帮助人力资源决策。一项应用涉及使用机器学习从就业面试回答中自动推断申请人的特征。然而，管理研究提供的有效性证据很少，可以指导组织决定是否以及如何最好地实施这些算法方法。为了解决这一差距，我们在模拟视频面试中使用封闭词汇文本挖掘来训练和测试机器学习算法，以预测受访者的自我报告（自动个性识别）和面试官评价的个性特征（自动个性感知）。我们使用 10 倍交叉验证来测试算法在两个评级源中预测大五人格特征的准确性。预测自我报告的交叉验证准确性低于使用社交媒体帖子中的语言作为预测因子的大规模调查。预测面试官个性评分的交叉验证准确性是预测自我报告的两倍多。我们讨论对未来研究和实践的影响。