Illinois professor reveals research into automated video job interviews | Illinois

(The Center Square) – Many companies are turning to automated video interviews, or AVIs, in an effort to save money, but do AVIs tell companies what they need to know about candidates?

That’s a question Rachel Saef, a professor of psychology at Northern Illinois University, set out to answer in research for the Journal of Applied Psychology.

Large employers use these “asynchronous video interviews” to narrow down candidates to a smaller group that they can meet in person. In some cases, the recordings will be viewed by the employer’s hiring managers. In others, the platform’s algorithms will assess the candidate based on what they say or even their facial expressions.

“Computers assess the characteristics and personality of applicants based on patterns of behavior,” Saef said. “These are behaviors that are not explicitly expressed in a traditional interview. It’s things like non-verbal behaviors like smiles.

It is proven that these technologies can contain biases likely to exclude certain categories of job seekers. The Berkeley Haas Center for Equity, Gender, and Leadership reports that 44% of AVI systems are embedded with gender bias.

Saef’s research showed that AVI personality ratings showed stronger evidence of validity when trained on interviewer reports rather than self-reports.

While developers are trying to eliminate bias and increase the reliability of the interview process, Saef said we still know very little about how AVIs are experienced by different categories of applicants and how accurate they are. .

“Here we see practice moving faster than research,” Saef said.

Research on the effects of IVAs on job applicants is in its infancy. The Harvard Business Review found that job applicants were confused about the type of interview they were being asked to undertake and often did not know how they were going to be assessed by the AVI. The lack of understanding reflects the widespread fear that job seekers are misinformed, leading to legal issues.

“We need to make sure it’s used in a fundamentally smart way,” Saef said. “Does it provide useful information about candidates and their job performance?”

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