Developing and evaluating language‐based machine learning algorithms for inferring applicant personality in video interviews

Abstract 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.