Personality Predictions from Automated Video Interviews: Explainable or Unexplainable Models?

Research Talk
David Stillwell, University of Cambridge

In automated video interviews (AVIs), candidates answer pre-set questions by recording responses on camera and then interviewers use them to guide their hiring decisions. To reduce the burden on interviewers, AVI companies commonly use black-box algorithms to assess the quality of responses, but little academic research has reported on their accuracy. We collected 694 video interviews (200 hours) and self-reported Big Five personality. In Study 1 we use machine learning to predict personality from 1,710 verbal, facial, and audio features. In Study 2, we use a subset of 653 intuitively understandable features to build an explainable model using ridge regression. We report the accuracies of both models and opine on the question of whether it would be better to use an explainable algorithm.