Surprisingly, the perceived gender of the voice had no effect on results.
The main difference seemed to lie in much higher numbers of women leaving
the platform after one or two poor interviews.
*"it’s not about systemic bias against women or women being bad at
computers or whatever. Rather, it’s about women being bad at dusting
themselves off after failing."*
On Jul 5, 2016 6:36 AM, "Romy Ilano" <romy(a)snowyla.com> wrote:
Maybe this is another reason why coed groups may
benefit Women more than
all women groups?
We built voice modulation to mask gender in technical interviews. Here’s
June 29th, 2016
Posted by [image: user]Aline Lerner <https://twitter.com/alinelernerLLC> on
interviewing.io <http://www.interviewing.io> 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. 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. *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.*
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. Interview questions on
the platform tend to fall into the category of what you’d encounter at a
phone screen for a back-end software engineering role, and interviewers
typically come from a mix of large companies like Google, Facebook, Twitch,
and Yelp, as well as engineering-focused startups like Asana, Mattermark,
After every interview, interviewers rate interviewees on a few different
[image: Feedback form for interviewers]
Feedback form for interviewers
As you can see, we ask the interviewer if they would advance their
interviewee to the next round. We also ask about a few different aspects of
interview performance using a 1-4 scale. On our platform, a score of 3 or
above is generally considered good.
Women historically haven’t performed as well as men…
One of the big motivators to think about voice masking was the
increasingly uncomfortable disparity in interview performance on the
platform between men and women. At that time, we had amassed over a
thousand interviews with enough data to do some comparisons and were
surprised to discover that women really were doing worse. Specifically, *men
were getting advanced to the next round 1.4 times more often than women.
Interviewee technical score wasn’t faring that well either — 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.
… so we built voice masking
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.
[image: Early voice masking prototype]
Early voice masking prototype (drawing by Marcin Kanclerz
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 interviewing.io sounds like. In the first
one, I’m talking in my normal voice.
And in the second one, I’m modulated to sound like a man.
Armed with the ability to hide gender during technical interviews, we
were eager to see what the hell was going on and get some insight into why
women were consistently underperforming.
The setup for our experiment was simple. Every Tuesday evening at 7 PM
Pacific, interviewing.io hosts what we call practice rounds. In these
practice rounds, anyone with an account can show up, get matched with an
interviewer, and go to town. And during a few of these rounds, *we
decided to see what would happen to interviewees’ performance when we
started messing with their perceived genders*.
In the spirit of not giving away what we were doing and potentially
compromising the experiment, we told both interviewees and interviewers
that we were slowly rolling out our new voice masking feature and that they
could opt in or out of helping us test it out. Most people opted in, and we
informed interviewees that their voice might be masked during a given round
and asked them to refrain from sharing their gender with their
interviewers. For interviewers, we simply told them that interviewee voices
might sound a bit processed.
We ended up with 234 total interviews (roughly 2/3 male and 1/3 female
interviewees), which fell into one of three categories:
- Completely unmodulated (useful as a baseline)
- Modulated without pitch change
- Modulated with pitch change
You might ask why we included the second condition, i.e. modulated
interviews that didn’t change the interviewee’s pitch. As you probably
noticed, if you played the videos above, the modulated one sounds fairly
processed. The last thing we wanted was for interviewers to assume that any
processed-sounding interviewee must summarily have been the opposite gender
of what they sounded like. So we threw that condition in as a further
After running the experiment, we ended up with some rather surprising
results. *Contrary to what we expected* (and probably contrary to what
you expected as well!), *masking gender had no effect on interview
performance* with respect to any of the scoring criteria (would advance
to next round, technical ability, problem solving ability). If anything, we
started to notice some trends in the opposite direction of what we
expected: for technical ability, it appeared that men who were modulated to
sound like women did a bit better than unmodulated men and that women who
were modulated to sound like men did a bit worse than unmodulated women.
Though these trends weren’t statistically significant, I am mentioning them
because they were unexpected and definitely something to watch for as we
collect more data.
On the subject of sample size, we have no delusions that this is the
be-all and end-all of pronouncements on the subject of gender and interview
performance. We’ll continue to monitor the data as we collect more of it,
and it’s very possible that as we do, everything we’ve found will be
overturned. I will say, though, that had there been any staggering gender
bias on the platform, with a few hundred data points, we would have gotten
some kind of result. So that, at least, was encouraging.
So if there’s no systemic bias, why are women performing worse?
After the experiment was over, I was left scratching my head. If the
issue wasn’t interviewer bias, what could it be? I went back and looked at
the seniority levels of men vs. women on the platform as well as the kind
of work they were doing in their current jobs, and neither of those factors
seemed to differ significantly between groups. But there was one nagging
thing in the back of my mind. I spend a lot of my time poring over
interview data, and I had noticed something peculiar when observing the
behavior of female interviewees. Anecdotally, it seemed like women were
leaving the platform a lot more often than men. So I ran the numbers.
What I learned was pretty shocking. *As it happens, women leave
interviewing.io <http://interviewing.io> roughly 7 times as often as men
after they do badly in an interview.* And the numbers for two bad
interviews aren’t much better. You can see the breakdown of attrition by
gender below (the differences between men and women are indeed
statistically significant with P < 0.00001).
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 interviewing.io experience.
A totally speculative thought experiment
So, if these are the kinds of behaviors that happen in the
interviewing.io microcosm, how much is applicable to the broader world
of software engineering? Please bear with me as I wax hypothetical and try
to extrapolate what we’ve seen here to our industry at large. And also,
please know that what follows is very speculative, based on not that much
data, and could be totally wrong… but you gotta start somewhere.
If you consider the attrition data points above, you might want to do
what any reasonable person would do in the face of an existential or moral
quandary, i.e. fit the data to a curve. An exponential decay curve seemed
reasonable for attrition behavior, and you can see what I came up with
below. The x-axis is the number of what I like to call “attrition events”,
namely things that might happen to you over the course of your computer
science studies and subsequent career that might make you want to quit. The
y-axis is what portion of people are left after each attrition event. The
red curve denotes women, and the blue curve denotes men.
See interactive graph with Desmos
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. How many “attrition events” does one encounter between
primary and secondary education and entering a collegiate program in CS and
then starting to embark on a career? So, I don’t know, let’s say there are
8 of these events between getting into programming and looking around for a
job. 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. If we
think about how many there are now, which, depending on your source, is
between 1/3 and a 1/4 of the number of men, *to get to pipeline parity,
we actually have to increase the number of women studying computer science
by an entire order of magnitude*.
Prior art, or why maybe this isn’t so nuts after all
Since gathering these findings and starting to talk about them a bit in
the community, I began to realize that there was some supremely interesting
academic work being done on gender differences around self-perception,
confidence, and performance. Some of the work below found slightly
different trends than we did, but it’s clear that anyone attempting to
answer the question of the gender gap in tech would be remiss in not
considering the effects of confidence and self-perception in addition to
the more salient matter of bias.
In a study investigating the effects of perceived performance to
likelihood of subsequent engagement
Dunning (of Dunning-Kruger fame) and Ehrlinger administered a scientific
reasoning test to male and female undergrads and then asked them how they
did. Not surprisingly, though there was no difference in performance
between genders, women underrated their own performance more often than
men. Afterwards, participants were asked whether they’d like to enter a
Science Jeopardy contest on campus in which they could win cash prizes.
Again, women were significantly less likely to participate, with
participation likelihood being directly correlated with self-perception
rather than actual performance.
In a different study, sociologists followed a number of male and female
STEM students over the course of their college careers
via diary entries authored by the students. One prevailing trend that
emerged immediately was the difference between how men and women handled
the “discovery of their [place in the] pecking order of talent, an
initiation that is typical of socialization across the professions.” For
women, realizing that they may no longer be at the top of the class and
that there were others who were performing better, “the experience
[triggered] a more fundamental doubt about their abilities to master the
technical constructs of engineering expertise [than men].”
And of course, what survey of gender difference research would be
complete without an allusion to the wretched annals of dating? When I told
the interviewing.io team about the disparity in attrition between
genders, the resounding response was along the lines of, “Well, yeah. Just
think about dating from a man’s perspective.” Indeed, a study published
in the *Archives of Sexual Behavior*
confirms that men treat rejection in dating very differently than women,
even going so far as to say that men “reported they would experience a more
positive than negative affective response after… being sexually rejected.”
Maybe tying coding to sex is a bit tenuous, but, as they say,
programming is like sex — one mistake and you have to support it for the
rest of your life.
Why I’m not depressed by our results and why you shouldn’t be either
Prior art aside, I would like to leave off on a high note. I mentioned
earlier that men are doing a lot better on the platform than women, but
here’s the startling thing. *Once you factor out interview data from
both men and women who quit after one or two bad interviews, the disparity
goes away entirely.* So while the attrition numbers aren’t great, I’m
massively encouraged by the fact that at least in these findings, it’s not
about systemic bias against women or women being bad at computers or
whatever. Rather, it’s about women being bad at dusting themselves off
after failing, which, despite everything, is probably a lot easier to fix.
1Roughly 15% of our users are female. We want way more, but it’s a
2If you want to hear more examples of voice modulation or are just
generously down to indulge me in some shameless bragging, we got to demo it
and in Fast Company
3In addition to asking interviewers how interviewees did, we also ask
interviewees to rate themselves
After reading the Dunning and Ehrlinger study, we went back and checked to
see what role self-perception played in attrition. In our case, the answer
is, I’m afraid, TBD, as we’re going to need more self-ratings to say
Sent from my iPhone
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