Artificial Intelligence in Automated Scoring of Video Interviews

Abstract

Abstract The use of artificial intelligence (AI)-scored video interviews has increased significantly in the past five years and gained mainstream momentum with the shift to remote prehire tools during the pandemic. Asynchronous video interviews (AVIs) hold the promise of a more efficient hiring process, that is more candidate friendly, with less bias than traditional screening methods, and that meets the measurement standards of the industrial-organizational profession. However, little published research has documented these benefits. This chapter summarizes much of the established research on AVIs and the AI methods to score these interviews, presents a methodology for designing autoscored AVIs, and discusses the latest published (and unpublished) psychometric results concerning reliability, convergent validity, criterion validity, and adverse impact. The chapter submits that these results are on par with the published research on structured interviews, and thus the scale and scope of using AVIs for prehire needs are above and beyond traditional assessment methods. The chapter also discusses the utility of multidisciplinary approaches to designing innovative talent assessments and reminds readers of the benefits of following IO assessment principles in the design of these novel approaches.