A relatively unknown startup “interviewing.io” conducted a study that reveals that women who’s voices were disguised as men’s actually did worse in the employment process.
“Interviewing.io is a platform where people can practice technical interviewing anonymously” in order to be judged not on prior credentials or superficial factors, but purely on “interview performance,” according to a blog post published earlier this week.
Many studies have tried to prove that there is an inherent gender bias in the tech world. Organizations have even formed to combat this purported favoritism. Aline Lerner, an employee at interviewing.io, was troubled when she discovered that “men were getting advanced to the next round 1.4 times more often than women” and “men on the platform had an average technical score of 3 out of 4, as compared to a 2.5 out of 4 for women.”
“Despite these numbers, it was really difficult for me to believe that women were just somehow worse at computers, so when some of our customers asked us to build voice masking to see if that would make a difference in the conversion rates of female candidates, we didn’t need much convincing,” said Lerner.
The startup built the voice obscuring technology within the app and tested 234 different interviews, roughly 2/3 male and 1/3 female.
“In short, we made men sound like women and women sound like men and looked at how that affected their interview performance. We also looked at what happened when women did poorly in interviews, how drastically that differed from men’s behavior, and why that difference matters for the thorny issue of the gender gap in tech,” Lerner explained.
The results are quite contrary to Lerner’s and many others’ beliefs. According to the startup’s own results, “masking gender had no effect on interview performance … if anything, we started to notice some trends in the opposite direction of what we expected” and “though these trends weren’t statistically significant, I am mentioning them because they were unexpected.”
“After the experiment was over, I was left scratching my head. If the issue wasn’t interviewer bias, what could it be?” Lerner wondered. Her only straightforward explanation: women were quitting the startup’s analysis services “roughly 7 times as often as men after they do badly in an interview.”
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