Automatically scored interviews: Incorporating AI and Preemployment Assessments

Career Level: Basic-level career services and university relations and recruiting professionals
Competency: Use of technology
Fee: $69 (member); $89 (nonmember);

Summary
Artificial intelligence and machine learning are increasingly being used in preemployment assessment, including to automatically score interviews. Much consternation and confusion around these technologies exist even though essays in standardized tests have been automatically scored for more than two decades. Dr. Hickman is a leading expert on the science of automatically scoring interviews and is collaborating with several organizations who sell such tools. In this session, the current state of the science and practice of automatically scored interviews will be discussed, including tips for preparing students for such interviews.

Following this program, you will be able to:
- Explain how machine learning is used to score interviews;
- Identify how to prepare students to succeed in automatically scored interviews; and
- Describe the benefits and drawbacks of automatically scoring natural language text.
Presenters

Louis Hickman, Assistant Professor, Virginia Tech
Dr. Louis Hickman is an assistant professor of industrial-organizational psychology at Virginia Tech and a Senior Fellow at University of Pennsylvania's Wharton People Analytics. His research focuses on applications of machine learning and artificial intelligence to organizational science and practice, including issues of bias and fairness. His work has been published in journals such as Journal of Applied Psychology, Personnel Psychology, and Organizational Research Methods. His research has received multiple awards, including a Society for Human Resource Management Foundation Award for his dissertation, Algorithmic Ability Prediction in Video Interviews. He serves on the editorial board of Journal of Applied Psychology and Organizational Research Methods. Additionally, he has chaired multiple symposia on machine learning and served as principal investigator for two grants from the Society for Industrial and Organizational Psychology—one addressing “Algorithmic racial bias in automated video interviews” and another applying machine learning to new workplace domains, “Automatic scoring of interpersonal assessment center simulations: Effects of reliability and saturation on validity.”