Machine learning in recruiting: predicting personality from CVs and short text responses - Grunenberg, Eric; Peters, Heinrich; Francis, Matt J.; Back, Mitja D.; Matz, Sandra C. (Frontiers in Social Psychology, 2024)
Assessing the psychological characteristics of job applicants—including their vocational interests or personality traits—has been a corner stone of hiring processes for decades. While traditional forms of such assessments require candidates to self-report their characteristics via questionnaire measures, recent research suggests that computers can predict people's psychological traits from the digital footprints they leave online (e.g., their Facebook profiles, Twitter posts or credit card spending). Although such models become increasingly available via third-party providers, the use of external data in the hiring process poses considerable ethical and legal challenges. In this paper, we examine the predictability of personality traits from models that are trained exclusively on data generated during the recruiting process. Specifically, we leverage information from CVs and free-text answers collected as part of a real-world, high-stakes recruiting process in combination with natural language processing to predict applicants' Big Five personality traits (N = 8,313 applicants). We show that the models provide consistent moderate predictive accuracy when comparing the machine learning-based predictions with the self-reported personality traits (average r = 0.25), outperforming recruiter judgments reported in prior literature. Although the models only capture a comparatively small part of the variance in self-reports, our findings suggest that they might still be relevant in practice by showing that automated predictions of personality are just as good (and sometimes better) at predicting key external criteria for job matching (i.e., vocational interests) as self-reported assessments.