Dive Brief:
- When interviewees with noticeable facial features such as moles, scars or birthmarks acknowledge their "facial stigmas," interviewers pay less attention to them, according to researchers from Rice University and the University of Houston's Hilton College.
- The researchers gathered 112 people who viewed short mock job interviews, listening to computer-mediated interviews while studying a picture of someone with a scar or port wine stain. Half of those posing as applicants said something about their facial features at the beginning of the interview. The other half made no mention of their facial features.
- Those interviewing participants who noted their facial features "were less likely to pay attention to it throughout the interview process," the report found.
Dive Insight:
The recruiting process should focus on candidates' skills and experience, but discrimination persists in various forms, despite the laws that prohibit it and the technology designed to eliminate it. Discrimination on the basis of facial features is not necessarily illegal; hiring managers, however, may need to work to eliminate their biases against those who differ from them for any reason, be it birthmarks, scars, style, speech, ability, race or any other factor.
For example, employers say they're committed to hiring veterans, yet a Duke University study found that recruiters often perceive candidates with military experience as impersonal and unemotional and therefore fail to hire them for positions requiring emotional intelligence. As a result, veterans often are assigned to positions that don't require much people interaction.
A series of technological tools have emerged to detect bias in recruiting and hiring. Last month, tech hiring platform HackerRank introduced a diversity and inclusion center to reduce hiring bias and help organizations build technical teams that are more diverse and inclusive. Researchers at Penn State University and Columbia University unveiled an artificial intelligence (AI) tool to detect discrimination based on legally protected classifications, such as race and gender in hiring, pay practices, policing, education admissions and consumer finance.
