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The red curve denotes women, and the blue curve denotes men.
Now, as I said, this is pretty speculative, but it really got me thinking about what these curves might mean in the broader context of women in computer science.
If that’s true, then we need 3 times as many women studying computer science than men to get to the same number in our pipelines.
Note that that’s 3 times more than men, not 3 times more than there are now.
is a platform where people can practice technical interviewing anonymously and, in the process, find jobs based on their interview performance rather than their resumes.
Since we started, we’ve amassed data from thousands of technical interviews, and in this blog, we routinely share some of the surprising stuff we’ve learned.
When the Bane mask thing didn’t work out, we decided we ought to build something within the app, and if you play the videos below, you can get an idea of what voice masking on sounds like. Also note that as much as possible, I corrected for people leaving the platform because they found a job (practicing interviewing isn’t that fun after all, so you’re probably only going to do it if you’re still looking), were just trying out the platform out of curiosity, or they didn’t like something else about their experience.
So, if these are the kinds of behaviors that happen in the microcosm, how much is applicable to the broader world of software engineering?
When I told the team about the disparity in attrition between genders, the resounding response was along the lines of, “Well, yeah.
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.
When an interviewer and an interviewee match on our platform, they meet in a collaborative coding environment with voice, text chat, and a whiteboard and jump right into a technical question.
Since we started working on interviewing.io, in order to achieve true interviewee anonymity, we knew that hiding gender would be something we’d have to deal with eventually but put it off for a while because it wasn’t technically trivial to build a real-time voice modulator.
Some early ideas included sending female users a Bane mask.
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In this post, I’ll talk about what happened when we built real-time voice masking to investigate the magnitude of bias against women in technical interviews.