We built voice modulation to mask gender in technical interviews. Here’s what happened.

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.

The setup

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, and others. For more context, some examples of interviews done on the platform can be found on our public recordings page.

After every interview, interviewers rate interviewees on a few different dimensions.

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 women1. 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.

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.2

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 experiment

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 control.

The results

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 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.

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.3

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 start.

 

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 on NPR 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 anything conclusive.

262 thoughts on “We built voice modulation to mask gender in technical interviews. Here’s what happened.”

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  3. I’ve recently read an article that studied how women will not stay as often in a team where they are significantly outnumbered. Perhaps you should modulate the voices of the interviewers, to see if that affects the attrition rate.

      1. @karen

        I’d wager that most western women are sexist, yes. We have feminism to blame for that. Just look at the presumption on which this study was done.

        Despite the fact that we have more legal rights and arguably more social favor than men, most western women have a strong distrust for the opposite sex. When feminists see less women in STEM, they don’t think what a rational person would think at first- that is, “maybe less women are interested in joining STEM fields, in the same way less men are interested in teaching children or becoming nurses”- they automatically assume that there’s some conspiracy being perpetrated by men to keep women down. Unfortunately they’ve failed to realize that this ideology is not “empowering” in the least, but quite the opposite.

        I have to give the author credit for at least accepting the statistics of the study, though- even though they go against the agenda of what they were looking for. Most feminists will never accept statistics. Just look at some of the ones in the comments here.

      2. karen straughan

        @Tiffany

        Off topic, but OMG, I hate this commenting app. (Must be a patriarchal plot to keep women like me in our place!) Why not go with Disqus? Everyone can manage Disqus, even rubes like me.

        As for your comment, yes, I agree. I’m very pleased with the fact that the researchers were honest in presenting their findings, and I don’t even have a problem regarding their speculation as to what the results mean (higher attrition rates for women). I disagree with their basic premise that the lower representation of women in certain fields is a “problem” that must be solved, but at least they’re not fudging their numbers to fit the narrative.

        I think, given the comments of MANY people here, there is no consideration given to the possibility (and I’m not saying this is a certainty, only a possibility) that women’s higher attrition rates in the face of difficulties or barriers might not be 100% about culture. I’ve seen many people suggest that perhaps more women drop out at this stage than men because they’ve had more “attrition-causing events” in the past. No one seems to be willing to consider that perhaps men and women have different thresholds regarding what they would see as an “attrition-causing event”.

        We know women apologize more than men do largely because they take personal offence over smaller slights than men. When a woman bumps you accidentally and apologizes, it’s because she would expect you to apologize if the situation were reversed and you’d accidentally bumped her. When a man bumps someone accidentally, he doesn’t apologize because he would not expect an apology if the situation were reversed. It’s kind of like both men and women adhere to the Golden Rule, but they have different standards as to how they would have others to “do unto them”.

        It’s entirely likely that men actually face more “barriers” and “obstacles” in this field than women do, and the results of this study certainly don’t refute that possibility. But we don’t raise our boys to ask for those obstacles to be removed, do we? We expect them to deal. Man up. Grow a pair. In the words of Gunny Highway: improvise, adapt and overcome, you sissies.

        This points to a very different set of criteria in terms of how men and women expect to be treated, with women having higher expectations of courtesy, politeness, kindness, friendliness, forgiveness, tolerance of failure and the rest, than men. And given the roles of sex hormones, I can’t imagine they don’t play a part in this.

        Can culture or socialization change this? Certainly, it can affect it, by either reinforcing it or working against it. But I can’t get behind the idea that the culture must change in order to make it so that women experience fewer of these “attrition-causing events”, because this only reinforces their sensitivity to them. It tells them that their feelings (taking criticism personally and being discouraged) are appropriate and should be indulged, which only lowers the bar as to what kinds of events they will find discouraging or upsetting. Then all you’re left with is a cascade effect of women becoming more and more sensitive to ever more trivial difficulties.

        I mean, honestly, my grandmother was a career woman ending up as manager of the general store and post-mistress of her town, all with a grade 3 education after being born into rural poverty in 1909. She’d tell these milquetoast women complaining about “subtle biases” and “microaggressions” these days to grow up, rub some dirt in it and go do something useful.

        It’s overcoming obstacles that builds fortitude and persistence, not having them removed for you. My grandmother would be rolling over in her grave if she knew the state of the modern western woman, who is the Princess and the Pea come to life.

      3. A very interesting line of thought.

        I have a wrinkle to add to it: Perhaps it’s too reductionist to say that higher female attrition rates in STEM fields are a call for the workplace to be “sissified”, for one obvious reason: Men and women fall on a broad spectrum, and STEM fields are geeky fields where intellectual rigor and emotional perceptiveness go hand-in-hand. Men and women alike need to learn how to work harmoniously with people who can be a good standard deviation above the sensitivity level of the general public. We’re not just talking about “apologize if you bump into them in the hall”, we’re on the level of “make sure you choose exactly the right words in your feedback to a comment on a pull request so you don’t accidentally invoke a jihad over code formatting between your lead programmer and your PM.”

        If a lot more women are dropping out of the field, this may be a legitimate sign, but of something else: The field is overpopulated with men (and some women) in middle-management positions who don’t know how to build a high-functioning team in general, and favor in others the skills that got them into management.

        Nothing is more refreshing on a team than working with someone who admits their mistakes, owns them, and works to fix and prevent them, humbly recruiting others as needed. How does that ability come across in the average job interview? It barely comes across at all.

      4. I’m not sure that’s a fair summary of the statement and we tend be very liberal with the term “sexism” these days. We all have inherent biases which sometimes exist on a subconscious level. People often feel more comfortable around people they can identify with and relate to. Things like gender and race can play a significant role in one’s confidence and we all know how important confidence is in an interview.

        It is a suggestion for an experiment which may well have equally surprising results. The original experiment was to test bias in the interviewers. It would be remiss not to test for biases in the interviewees, especially considering it only requires a slight modification to the original test.

        In fact I just discussed the idea with my girlfriend who is also in tech and she said a female interviewer would definitely make her feel more at ease.

      5. karen straughan

        @Estelle

        There’s a very interesting cognitive difference between men and women when it comes to gender identity. There was a study done back in 2004 called “Gender differences in automatic in-group bias: why do women like women more than men like men?”

        http://www.ncbi.nlm.nih.gov/pubmed/15491274

        It could, more reasonably, have been subtitled, “why does everyone like women more than men like men?” In 4 of 4 experiments designed to detect positive attitudes and feelings of affiliation toward men and women, women strongly favored women. In 3 of 4 experiments, men also strongly favored women.

        This study, done by a feminist grad student at the University of Waterloo, seems to replicate this: https://uwspace.uwaterloo.ca/bitstream/handle/10012/6958/Yeung_Amy.pdf

        In this set of experiments, the subjects (men and women) were shown scenes of men and women interacting, then were asked to answer questions that would indicate hostile sexism on the part of the man, such as “how likely would this man be to abuse his wife?” or “how likely would this man be to deny a woman a promotion based on her sex?”.

        In situations where men treated women better than they treated men, they were judged to be not hostilely sexist. In situations *where they treated women equally compared to how they would treat men* they were judged by the subjects to be hostilely sexist. The only mitigating factor was if it was made clear to the subjects that the man was treating the woman equally in order to promote women’s equality (you know, for a benevolently sexist reason), at which point, he was seen as less hostilely sexist than the second guy, but still more hostilely sexist than the first guy.

        Now, I’m not going to tell you that sexism doesn’t exist, or that people in general (it’s almost never just men) have different expectations of men and women. They absolutely do.

        But men seem to lack a psychological mechanism for automatic own-group gender preference (makes perfect sense in terms of evolution), which makes the idea that men automatically favor men over women in everyday situations or contexts a highly dubious proposition. In fact, it throws the entire “patriarchy” narrative into question. A given woman seems able to make the leap from “I am a good person” to “women in general are good people”, while men simply don’t make that leap. Men do not tend to say, “I am a good person, therefore men in general are good people.”

        “In fact I just discussed the idea with my girlfriend who is also in tech and she said a female interviewer would definitely make her feel more at ease.”

        So what you’re saying is that women are sexist. You’ve only proved my point regarding the fact that both men and women have more positive feelings about women.

        One interesting thing, though. There’s also been research done on mentoring and gender in academia (I don’t have it to hand, but could dig it up), and it shows that even though your friend would feel more at ease with a female interviewer, she’d probably get a poorer score. It is MEN in academia who are more likely to positively mentor female grad students and underlings by talking them up, giving them credit, etc. Female academics give less positive support to female grad students and underlings on average.

        What is even more interesting is research done into photos and resumes. HR is mostly women, and if you’re an attractive man, putting your photo on a resume will make it more likely to get past HR than if you had no photo or were unattractive, but if you’re an *attractive* woman and include your photo, it will be more likely to be binned before you get to the interview stage than if you had no photo, or were unattractive.

        If your friend thinks she’s going to get a fairer shake from a woman than a man, she’s mistaken.

      6. I think you misunderstand my dispute. I believe what you are saying is largely correct and well researched. However I don’t think “women are sexist” is the conclusion to be drawn from my statement.

        For example, whenever I meet someone from the same country as me, we tend to click more easily because we’ve often shared similar experiences growing up. I wouldn’t consider myself racist for having more in common with people from my country. I would be just as willing to speak to anyone else and still try just as hard to connect with them.

        Being able to relate to someone more easily because they share similarities to you is different from being prejudiced against those who don’t share that similarity. Whether or not she would be better off being interviewed by a man or a woman is a different debate. I only argue that it’s not sexist to worry about being in a male dominated work place. Not knowing if you’d fit in or if you’d be treated differently. Sometimes just a tiny bit of positive reinforcement makes a huge difference.

        Human beings aren’t as rational as we like to think. We worry too much about things we ought not to and worry too little about the things we should do. We draw conclusions from insufficient evidence and disregard things that have overwhelming evidence in their favour. There is often a large gap between the truth and what we perceive to be the truth. I think a more accurate summary would be “sometimes, some people worry about things”. As you’ve meticulously pointed out, some of these things aren’t worth worrying about when you look at the data but that doesn’t change the fact that some people do sometimes worry about such things, especially when they haven’t seen the data.

        This doesn’t affect the validity of your statements. I only make a fuss over it because I’ve noticed that people are becoming desensitised to terms like “sexism” and “feminism”. If we keep using the word “sexism” where it isn’t due it will only increase the stigma against feminism.

      7. karen straughan

        @Estelle We are all sexist to some degree in that we perceive and treat men and women differently. In my view, these are inherent biases that are biological in origin and will therefore never be completely eliminated. I don’t think that’s necessarily a bad thing–I think it’s a morally neutral aspect of the human condition.

        My bone to pick here, as it were, is the currently fashionable idea that sexism stems from some mythical “patriarchy” that seeks to benefit men as a class at the expense of women as a class. This idea should be relegated to the same obscure corner of society where 9/11 trutherism and birtherism exist. It is little more than a conspiracy theory.

        In nearly every single metric we would use to demonstrate systemic cultural and institutional bias against blacks compared to whites in the US, men fare more poorly than women. From early childhood onward, society shows much greater concern for the safety, health and wellbeing of women and girls than it does for men and boys. Simply put, we treat our women less harshly than we do our men, and this is generally true of all cultures, currently and historically.

        I mean, think about it. If you were to look at the statistics on violence, men and boys are victimized at least 3 times as often as women. If you look at family violence in particular, men and women are roughly equally likely to be victims. If you look at children, parents and caregivers hit boys 2 to 3 times as often as they hit girls, starting before age 1. While more girls are sexually abused than boys, more boys suffer the most extreme forms of sexual abuse than girls. No matter the gender of the perpetrator, violence is more likely to target men and boys than women and girls. And perhaps most disturbingly, children are more likely to be victimized by violence than adult women.

        Yet our only demographically specific violence laws and policies address violence against women–who are already the safest demographic in society.

        At the same time, we have cultural narrative that violence against women is normalized (or even required) by society, that it’s a patriarchal mechanism designed to subjugate women for men’s benefit. This culture that has created a special set of federal laws and policies to protect women from violence even over and above protecting children, is, we are told, inherently and pervasively misogynistic and supportive of gender-based violence against women.

        Looking at the situation as a whole, if one were to apply Occam’s Razor, the assumption would be that society is not only not misogynistic, but that whatever biases do exist exist in the direction of society caring more about women than men, or even children. Our conclusion would be that society favors and elevates women, not that it hates and oppresses them while privileging men.

        But that’s not the general consensus, is it? Ironically, in my opinion, I believe that sexism is the root of this contradiction. We feel as if we cannot ever favor women enough, therefore we *must* be disfavoring them. That, Estelle, is part of the sexism I’m talking about.

        It is so deeply ingrained in our psychological wetware, that we will go to great lengths to deny the mere suggestion that we treat women better than we do men in many, if not most areas, that we are biased in favor of women rather than men, and that until every difficulty and every bit of suffering has been removed from the life of every woman in society, that society is not merely misogynist, but is based on misogyny.

        Look at some of the comments here, trying to find some explanation, ANY explanation for the unexpected results of this experiment. We have people suggesting that gendered speech patterns “tipped off” the interviewers and skewed the results, even though women who sounded like women did better than women who were made to sound like men, and men who sounded like women did better than men who sounded like men. In other words, female voices did better compared to male voices whether there was a man or a woman behind that voice.

        We have people suggesting that women have probably suffered more “attrition-causing events” by the time they made it to this service, and that must be why they were more willing to give up.

        We have people claiming men are privileged in tech and should be sensitive to the needs of women, even though the results of this experiment would suggest that whatever small bias may exist in interviewing exists is in women’s favor (which means that men are actually disprivileged in this situation).

        We have people scrambling to explain how systemic discrimination against women in tech really does exist even in the face of evidence suggesting not only that it does not, but that whatever discrimination does exist is in women’s favor.

        I’m constantly flabbergasted by this tendency toward mental gymnastics people engage in based on the premise that all roads lead to misogyny. If that is not sexism, then what is?

      8. Happosai 八宝斎

        @gww Indeed! The patriarchy has grown so devious as to employ women as it’s primary footsoldiers now. The poor dears!

  4. So when a person who uses vocal fry (like Aline), which is a linguistic trait highly associated with women, or other phrases, pauses, rhythm, and word choices that express femininity, has her voice modulated to sound like a man, she does slightly more poorly than other women? A person who sounds like a man but speaks like a woman does more poorly than a person who sounds like a woman and speaks like a woman.

    But when a person has a voice that sounds female but his interests, phrasing, tone, rhythm, etc dog whistle to being male, he is rated slightly higher than a male voice that sounds like a man?

    It seems to me that your test is showing a preference for male gender-based values, independent of pitch and biological sex. John Smith’s lamentable example above from the architecture and construction world is a great example of this bias. He has no value for the skills the women brought to the table in communication, making sure everyone’s needs were met, collaboration, sensitivity to the building’s use in it’s design and implementation, and so on. He judged the women’s social collaboration as making men do the hard decision making, with consensus being something he opposed as effeminate and therefore unprofessional. Women having emotions were deemed intentionally manipulative, conniving even. “Macho men,” in other words those with traditionally male behavior, however, were unable to “help themselves” in yielding to the women’s femininity. So he sympathizes with men responding in a traditionally male way while demonizing women acting in a traditionally female way and believes the workforce suffered from a positive integration of the feminine and masculine traits.

    Would John Smith consider a poll of clients to see their satisfaction levels on projects with women or without women to see if end level satisfaction was higher, even if there were more corrections needed, when the communication and collaboration women brought to the table was a factor? He’d undoubtedly dismiss it as men unable to help themselves in condescendingly overrating the conniving women in order to avoid an emotional meltdown from expressing displeasure, as there couldn’t be an inherent value in something not in his own checklist of personality, traits, values, or skills.

    I imagine if John Smith were one of your interviewers and he heard a woman who dog whistled to him through aggressive, egoistic, traditionally male phraseology, which is a male biological trait independent of culturalization, then he would take note and increase his measure of esteem for that woman who talks like a man (independent of pitch) above his general poor esteem for feminine traits in the workforce. He’s looking for someone to make the hard decisions by aggressive fiat, not someone who will go around consulting stakeholders to make sure consensus is reached. And, a person he perceived as male would likewise drop in his estimation when the man had the right pitch but talked like a woman, or like Aline with the vocal fry who “totally” did not sound “like a dude.”

    Add in the wonderful point you uncovered about sensitivity leading to higher attrition among women, along with many of the great points others have made above (including, especially, the discouragement women get from being told they’re likely to face bias or fail–which there are studies to support both regarding gender and race), and the dynamics become multivariable and complex again. I agree that there’s a lot more that needs to be done.

    1. “male based values” yeah, no… Otherwise either female speech patters with female voices are preferred or male speech patters with male voice. If that was the case people would be fighting over trans-female candidates or feminine male candidates, which isn’t the case.
      Despite all the effort to show that there’s discrimination against women with abundant failures, proving discrimination against people who don’t conform to usual gendered patters of speech and mannerisms, specially men, is astonishing.
      And it’s clearly not about “male values” because those values don’t coincide with male values, only coincidentally with “female good” and “male only good when useful but not to be point of making me jealous”, or more precisely, they coincide with female values.

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  6. The modulated voice sounded rather artificial/”roboty” and lower quality to me.

    So it seems plausible that interviewers are simply more likely to hire someone whose voice sounds natural and clear and is thus easier to understand, than someone whose voice sounds electronically processed and less clear.

    1. In the article it says that some people were made to sound processed/modulated but without changing gender as a control to eliminate this possibility.

  7. Computers must be gender-biased

    So let me see: you set up an elaborate project that was supposed to prevent the alleged discrimination and the whole thing blew up in your face, proving that there isn’t any gender discrimination in the first place! Ha ha … Astonishing that the assumptions of your leftist ideology did not match with the reality. Maybe the computers themselves somehow became gender-biased, hm? You have to investigate that next.

  8. Carol Dweck’s Mindsets or it’s more academic counterpart Self-Theories might be worth a read.

    She’s done a lot of research into how we react to failure.

  9. I’m wondering if some of the initial attrition rates between the sexes may be caused not by gender but by societal norms of gender in a particular region?

    So I’m wondering if you would add a regional question into your analysis if it would show anything.

    I’m also wondering if previous life experiences may also have a factor. Such as, what other activities do the interviewees participate in? Sports, Art, Music.

    I’m not sure if this is bias on my part but where I am from it is more common for males to participate in athletics. I’m wondering if that may also be a factor as well, I say this because in athletics many are faced with failing but are commonly taught not to give up.

    But this is also something that is taught in many extra curricular activities during someone’s education.

    1. Only a bigger study can show, but yeah, seem the difference in resilience is mostly the result of innate human bias favoring females. Basically pampering hampers development.
      Or more precisely, society amplifies innate differences.

  10. Did you think to analyze the text of the interviews to see if there was an indication of gender in the way someone spoke. There could have been a positive bias towards a more male textual representation.

    1. karen straughan

      @Randy

      So what about the men who did slightly better with their voices modulated to sound female than when unmodulated?

      That was the finding. Women made to sound like men did worse than when unmodulated. Men made to sound like women did better than when unmodulated. The difference was slight, but there.

      There’s plenty of evidence out there regarding an innate general preference for female voices. This video cites a lot of the relevant research (in this case, regarding the voices of digital personal assistants). https://www.youtube.com/watch?v=NExaO5NTCz8

  11. So many comments from people who missed the key takeaway (which was conveniently bolded for readers):

    “Once you factor out interview data from both men and women who quit after one or two bad interviews, the disparity goes away entirely.”

    I’m sure there may be underlying cultural elements at work that result in more women quitting after bad interviews, but it gives this recruiting platform a clear way to help: reach out to anyone (male or female) who’s had a poorly-rated interview and encourage them to continue. Sure, it may come off as self-serving (“Hey, keep using our product!”), but encouraging this kind of resiliency could make a positive impact.

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  13. Interesting… always impressed when scientists show results that don’t match the hypothesis.

    I would love to see more studies like this, would need to:
    –Be triple-blinded (researchers, interviewers, and interviewees)
    –Larger sample size (esp. female)
    –Mitigate the possible attrition issue by not showing the results to the interviewee until X interviews are completed

  14. Pingback: There's No Hiring Bias Against Women In Tech, They Just Suck At Interviews – Breitbart News | Bcst Connect

  15. I spoke to a few music teachers who told me that girls can not take criticism, or pressure.
    Boys can handle pressure and criticism without a complete breakdown.

  16. Pingback: No Hiring Bias Against Women In Tech, They Just Suck At Interviews – Breitbart News

  17. I would absolutely have predicted this result, because contrary to feminist doctrine, women are if anything favored in the IT field and generally treated a little better than the rest of us. The strange thing is that anyone should think it’s depressing if women don’t want to be in IT as much. For goodness sakes, that’s not a moral failing.

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  19. There are, in fact, differences in the ability distributions between the sexes that translate into a massive male overrepresentation at the upper ranks of ability, as Science vs. Feminism ably documents: http://www.sciencevsfeminism.com/the-myth-of-equality/sex-differences-general-intelligence/

    These cognitive differences explain why males dominate the top levels of achievement in all fields as well as the upper strata of all known human societies. Claims to the contrary rest on tests that are heavily sex-normalized and fail to account for the differing developmental trajectories of males and females.

  20. This article, while having good information,
    I view that it is strategically void.

    The human race is wildly successful… for 13,000 years ago.
    The historic selection doesn’t, however, get us to Alpha Centauri,
    …… as it inhibits 1/2 the population.

    There is the historical selection results:
    ..1. physical size differential
    ……testosterone is a much stronger muscular steroid than estrogen.
    ..2. the hormonal response to environmental conditions
    ……testosterone causing “elevation of mood”…. if a situation is “won”
    …… “take the hill” and “winning is the only thing” are mood response driven
    ..3. the early neural net programming of “blue blanket” vs “pink blanket”
    ……that has a reality based stress response basis.
    ..4. the mating behaviors that are wildly successful
    along with “modern” brainwashing.

    So, is there a “confidence” issue?
    Sure, for those who “winning is the only thing” getting a “high” are far more likely to do
    …”just about anything” to win.

  21. The fact is that voice pitch is not the only thing that helps us to identify gender. I’m working in a company which deals with different voice conversions: high-low pitch, male-female, all this stuff. Every time I hear a recording with female voice converted to male (voice sound is changed but the intonations and the whole manner of speaking stay the same), it feels very weird. I hear a male voice but I also feel that men don’t speak like this. Never. Well, maybe stereotype gay men in some movies. And it seems to me that this manner of speaking matters for us much more than the sound itself.

  22. “to get to pipeline parity”

    This is sexism against men. Nobody should try to get any kind of parity in anything. That’s social engineering. Why isn’t anyone concerned with parity for men in nursing, veterinary, dance etc.? Because those don’t pay much. People only care about trying to force women into higher paying jobs.

    1. You are arguing from the wrong premise.

      “People” are not concerned with the STEM imbalance. Politicians, CEOs, and hiring managers are, because they need more and better STEM workers. If dancers and veterinarians were in similar demand, you would see the same attention paid to them.

      This is a hugely growing field. If women and men are being rejected on the same terms, but the women are more discouraged by rejection, then politicians, CEOs, and hiring managers would like to find a way to turn that around, because it means they are losing perfectly good candidates.

      What would be helpful here is another landscape to compare it to: Is there a job that equal numbers of men and women apply for, that men drop out of more quickly? If there is, what accounts for that? If there ISN’T… If there is no job anywhere on Earth that men drop out of competing for sooner… Well, that would be quite interesting. But it would also totally invalidate the findings of the study above.

    2. Actually we should, we need more technical experts and we milked all the men from the pool for it beyond what’s reasonable to expect, all that’s left is a female pool and learning how to milk it for all it’s worth. Well, not parity but certainly more than this.

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  24. Werkof Rodann

    Love all the people flailing their arms desperately looking for other ways discrimination was actually present. It’s got to be that the female-male modulator was worse quality! Or that speech patterns between men and women are different and this is where the discrimination happens!

    Look ladies, if you have to keep coming up with more and more difficult to disprove means for discrimination each time evidence comes out supporting the opposite, you’re probably on the wrong side of the argument.

  25. “Contrary to what we expected (and probably contrary to what you expected as well!), masking gender had no effect on interview performanc”

    why is that the first assumption ? Im happy that you came around realizing that its not about inherent bias, but how the fuck did we get to a world where this is the basic assumption ?

    Imagine I had two race horses, with one consistently, for years, beating the other on a 500m run by around 10 senconds.
    would your primary assumption be:
    1- the losing horse is just not as fast even with the same raining regime, maybe they are built differently from birth.
    2- the horse racing society has conditionned me to think that the losing horse is wining when actually both horses have the same time and im probably a bigot because the wining horse is like green while the other is cyan or something.

    1. Or 3- the losing horse has been taught from birth not to run too fast because it’s undignified, and has been made to lose over and over in training because it wasn’t expected to become a champion, and has been pushed to spend hours and hours being groomed because people like to look at how “pretty” it is rather than spend time practicing running fast….

      1. Then 3b, the winning horse feels sad that he doesn’t ALSO get the grooming and attention of the losing horse, and comforts himself by becoming a brony 😀

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  27. I really don’t care for this question man vs woman. Because if I cared it would probably meant that I am biasing for a side, which I don’t.
    I know some women that kick some butts on development. They are awesome and they are widely recognize by their abilities.
    But for every woman like this I know at least 5 men with same ability or better.

    This is just my experience over 10 years.

    1. Caring about gender disparities really isn’t about bias, it’s about being aware of privilege. Power isn’t good or bad, but it’s important to be aware of. When white men speak in a meeting, more people listen, and listen more closely. It’s unfair and it’s something we as a society should work to change, but right now, it is what it is. Being aware of your privilege and speaking up for people who don’t enjoy the same privileges you do is a key way for you to demonstrate that you care about people, and understand society’s bias.
      This article actually had a really good point about attrition events too – a lot of women face A LOT of discrimination in STEM fields – in school, in recruitment (venues and processes), in interviews, in jobs, etc. Knowing WHY you know that many men with the same ability or better – and hopefully employing strategies to mitigate that disparity – is really the core question.

      1. “It’s about privilege. Women are soooo discriminated against, even though controlled experiments prove otherwise.”

        Lol. You’re like a caricature.

      2. Among men in the workforce, being a man is a privilege, because you are judged along the same axes as “everyone else”. Being a woman in a male-dominated workforce is one hell of a lot harder, especially because of men who tell themselves they are not biased, and then refuse to examine their own biases when they come into play. Like you, perhaps.

        In my 20+ years in this industry, the most common examples I’ve seen are from mid-level male developers who overreact to criticism from female developers as though their manhood were on the line, and then fail to communicate with them like they do with other co-developers because they are afraid of how it makes them look to the other men, thus isolating the female developer from the team and forcing her to work twice as hard to get things done.

        The ugliest examples I’ve seen are from men who would decorate their open-floor-plan workspace with figurines of busty, barely-dressed anime chix, men who would talk about how hot-or-not the female developers are, on the clock at work, and my personal favorite, a man in a group meeting who turned to his co-worker, just after she’d made a forceful point about keeping unstable code on side-branches, and said “you’re cute when you’re angry.”

        You try dealing with that shit for your whole career, and tell me it’s not a _privilege_ to never ever worry about it.

      3. If the worst part of work is how people judge you then you are probably not committing to work.
        Wanna more women into steam? Take away optional gym classes and put obligatory physically and mentally taxing charity work that can fail your year instead from a very young age.

      4. When attempting to perform legitimate research, addressing bias is crucial to achieving some sort of realistic understanding. Dubious pseudo-sociological hypotheses about ‘privilege’ have no place.

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  29. Pingback: What Happens When Gender is Masked in Tech Interviews? You Might Be Surprised – Techvibes

  30. One factor that you might look into, especially when it comes to attrition events, is how people respond to rejection (or perceived rejection), and also, how you inoculate (i.e. prepare) people for technical interviews. I’ve never held a purely technical position, but when I did a technical interview, I thought that I completely bombed it. What I learned later was that it was an interview designed to test my limits and how I react when I don’t know something, rather than an interview designed to test my knowledge. I later learned that the interviewer was *really* impressed with my interview, to the extent that she offered to recommend me for other similar jobs in the future (turns out I was overqualified for the position).
    I’m guessing that by telling people you might be masking/manipulating voices, people probably eliminated any bias that would normally be there. Did you compare the data from your study to your normal gender breakdown/data?

    1. I like the idea of inoculating interviewees against rejection. If interviewing.io isn’t already doing that, you should suggest it! Or if they’re not doing it well, suggest how they could improve.

  31. Did you factor in the interviewees’ experience with interviewing.io, whether for the women it was a ‘last resort’ in their job search versus a ‘first resort’ for the men? That is, for the female interviewees, was this interview among the last in a long line of interviews, whereas for the male interviewees, was it among the first, if not the first, interview? I wondered if the tendency to use such a platform has this phenomenon as a difference between men and women in the job search process.

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  33. Hmm.
    “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.”

    “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.”

    “As it happens, women leave interviewing.io roughly 7 times as often as men after they do badly in an interview.”

    “Once you factor out interview data from both men and women who quit after one or two bad interviews, the disparity goes away entirely”

    But we’re told that women drop out at 7x the rate of men after a poor interview. So to translate your sentence into something realistic: “After we got rid of a significant number of poorer performing women, and almost none of the poorer performing men, what was left was a pool of people who performed roughly the same.”

    Let me express it for you in mathematical terms:

    The standard deviation between women is less than the standard deviation between men. This means that in any “pool” where entry requires above average performance on characteristics whereon average men do at least as well as women do (and intelligence is such a case), you will find more men at the positive extremes than women. So in any environment where honest evaluation takes place, men will do better than women, and will succeed more often than women.

    Now, you might want to claim that it’s better to have more “women in tech”, even if that means having dumber, less qualified programmers who write worse code. And if you want to make that argument, I’ll be happy to listen and respond.

    But can we please drop the fantasy that there aren’t “enough” women in STEM because of sexism?

    1. There seems to be a flaw in your analysis. The people dropping out after one unsuccessful interview aren’t “the poorer performing people”. This is a group that drops out most easily – which may or may not have any correlation with their quality as engineers, professionals, or even their ability to write decent code.

      1. One might claim that perseverance is an important trait of a good software engineer
        One might also suspect that perseverance is correlated with intelligence

      2. The definition of insanity is doing the same thing over and over again, but expecting different results. – Albert Einstein.

      3. karen straughan

        “The definition of insanity is doing the same thing over and over again, but expecting different results. – Albert Einstein.”

        I lifted weights for a whole 15 minutes, and didn’t look like Jason Statham by the end of it! What a rip off!

        Correct me if I’m wrong, but people coming to this site do so because they’re applying for (or soon will be) jobs in their field. They want to get feedback on their performance in a typical interview *before* they risk poisoning any major wells.

        Practice is not insanity. The whole point of this exercise is to find out what you are and are not doing right, and work on improving your performance. The point is NOT to keep doing the same thing that doesn’t get you hired, but to find out what you need to improve on.

        Women are more likely to give up on the process after 1 or 2 bad performances than men are. Men are more willing to stick with the process of improving their performance in preparation for real life interviews.

        That’s not insanity. That’s how you get a job.

      4. Karen, my reply was very specifically directed at the comment “One might also suspect that perseverance is correlated with intelligence,” which insinuates since women don’t persevere in this exercise, that they are not intelligent.

        Of course interview practice can help get you a job. Asserting that a measure of how many times someone comes *here* to practice (perseverance) is a substitute measure of intelligence is not a warranted assumption.

      5. Talk to any programmer and you’ll hear that programming itself is insane. You troubleshoot until it works and sometimes you don’t get to know why it worked because the code is too big and there’s not enough time.
        Coding is knowing enough about something unintuitive that the average person thinks you sound insane and/or is unable to empathize with you and at the same time knowing that you don’t know nearly close to enough.

        Besides your quote being miss-attributed, coding does sound like insanity to the average person, so does hard sciences.

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  41. Have you studied the transcripts? Do women’s speech patterns, despite voice modulation, have less authoritative sentence structure, more hedging, less definitive word choices? You can give the same answer, but do it in ways that range from sounding like you’re guessing to sounding like you think the questioner was stupid for even posing such an unworthy query for your talents.

    1. Why is it so offensive or unacceptable that women are just not as interested in programming. They arent raised to be less curious in the US. When i was a teen in the 90’s lots of guys were taking pc’s apart and making little apps to do annoying things on aol. Because thats how we are. Women used aol just as much to socialize but never met one that was pirating softeare or learning to hack or taking apart hardware and our fathers didnt encoursge it since it wad totally alien to them! If anything they saw it as “faggy” and too intellectual but to us it was the same as them working on a car and enjoying it. Mem are often actually more skilled at development because it requires massive amounts of time and sacrifice, isolation, and the interest to do it as a hobby at first. And its not a matter of intelligence, its a matter of interest leading to practice leading to skill. There are some amazing females in code and they often agree that to allow unskilled people in the field because of gender gap is insulting to us all and the intense pride we have in our work. The reason tech is at the forefront of the job market is because the ppl involved are beyond passionate and obsessed with the work. If you bring identity politics into it, you will corrupt the best thing the modern world has going for it and it will hurt many other political goals by disenfranchising those who spent endless sleepless nights doing this stuff and could have written encrypted chat for revolutionary groups or whatever. This needs to remain a merit based field. We do not need more women in any way shape or form. If she has the skills, let her in. No other way is acceptable and the backlash will be huge. Calling us devout coders sexist wont justify a quota. And unless someone wants to do this difficult and stressful work, do not encourage them to. You might ruin their lives by forcing them into a field that consumes your entire mind at times and not just a few hours while clocked in. I think the only sexist thing i can see is wanting more of your tribe in a respected field for no reason but their gender. Its tribal and stupid and diversity is only helpful intellectually. Females nor males can add anything because of their genitals. Let coders code and dont piss off a very passionate subculture which insiders only can even understand anyway! If you dont write code, do not talk about code or what the dev world needs. We need to be left to ourselves to keep making innovative software and connecting the world. If we need something well let you know

      1. Before there were home PCs there were approximately equal numbers of male and female hackers. Home PCs were marketed to boys because toy and game manufacturers always marketed separately to boys and girls. Within 5 years male undergraduates were coming to college with more skills and leaving female students unable to compete on a level playing field. The discrepancy dates from sexist decisions made 25 years age and not inherently different skills.

        http://www.npr.org/sections/money/2014/10/21/357629765/when-women-stopped-coding

      2. You just compared women who attend college for computer science for programming in the 1980s (including introductory courses, in which MANY people tend to drop out, many males included), to being “hackers.”

        There were more women who attended computer science classes in the 80s, back when it was the advent of programming careers and when personal computers were new.

        There generally were no biases against women about the matter of programming jobs, because it wasn’t a wide interest. Computer jobs were un-sexy, compared to today’s geek-chic era, and it was all a new frontier, with relatively fewer people even qualified for tech back then, compared to today’s rife “interest” in tech.

        One of the most prominent computer scientists even back then was a woman–a seasoned pioneer by the name of Grace Hopper, who worked for the military and helped advance computer programming (also coined the term “bug”).

        What happened is that as the industry emerged, and the tech industries started to see more male pioneers who spent years searching for success (you might have heard of companies like Microsoft…took them 12 years to finally become a success), and it became clear that this thing was taking off fast, and would need people who can invest the most energy and interest in the industry, males proved to be most of those who remained with it.

        Creations like Microsoft and Apple weren’t created to spite women. They were created by curious and pioneering men, which inspired tons of other males. Same’s true with gaming. It wasn’t established to spite women. It was generated by those who created and contributed–and they tended to be men.

        Men tended learned this stuff as self-motivated, curious teen boys, who spent much of their youth creating primitive homebrew D&D computer games and hacking minor systems for cheap thrills, often getting years of early experience by the time they entered college.

        Most women, on the other hand, tended to get their first taste of computer science while in college, being young and looking at the prospects of computer science, and you saw waves of young women entering introductory courses. But soon, many found out that this industry wasn’t for them-particularly during its unsexy infancy.

        Per every introductory computer science course, you’ll find plenty of men and women alike dropping out of the introductory course after a while, realize that it’s just not for them, or finding something else of stronger interest to them. The majority of those who stay tend to be male, and often the ones who got a head start as kids.

        When I was 13, I had already learned 2 languages, including the “difficult” C++. By the time I was an adult, I was already well-ahead of those just getting their first taste in college. My dad’s a programmer, but I learned on my own accord, since he was rarely home enough to teach me. Just like Bill Gates and Steve Woz, I got started young, based on my own draw to the field by passion–and NOT as some look at what could be a good job.

        Women waiting around to be “inspired” to work in this field are waiting in vain. Whether male or female, if you’re not personally drawn to the work, it WILL be a chore for you, or you WILL become one of those job-for-job’s-sake programmers that nobody likes to work with because your work’s noticeably lazier.

        It just turns out that, for whatever general reasons, males seem to exhibit earlier passion for this stuff. I find this is a VERY common back story for most guys my age, in my line of work.

        You’ll be much harder pressed to find young women with similar backgrounds–though, I’ve met a few. Just a few, though. Even then, they tended to be second-generation programmers who likewise took around computers and did homebrew, like myself.

      3. “My dad’s a programmer, but I learned on my own accord, since he was rarely home enough to teach me.” May I ask who was with you at home then?

      4. I counter your anecdotes with my own: Growing up I had a number of female friends who were eager software pirates, several who built their own PCs, and one in particular who was an absolute genius with a TI-85 calculator and had a postdoc-level grasp of advanced mathematics and information theory while she was still in high school.

        That girl in particular went to CalTech, then burned out for a while and went into child development, then turned a 180 again and became a driver-level network optimization engineer for Facebook, and her career has moved up from there. This is not because of the happy and welcoming cadre of brogrammers that preceded her … but more IN SPITE of their obsessive and overeager attention due to the fact that she was female.

        If you think nothing needs to change in that environment for ALL real computer geeks to participate, you have your head in the sand.

      5. RationalistFaith

        How dare you use logic and suggest that women’s 1-2 weeks PMS’ing and their periods that affect their brain from thinking logically is an ISSUE?!!! /s

      6. Fabricus Maximus

        The fact is, as you said in your first sentence, numerous far leftists (or CTRL-Leftists) can’t be reasoned with in regards to women’s choices.

        Women can be free to choose, as long as they choose the only option available to them. IOW, feminism. All other “non-choices” are nothing but chalk full of ideological apostasy and betrayal.

        We can’t have women staying home to care for their children and lower the overall household bills for things like child care can we? Feminists seem to think their ideology is too important, TOO BIG to fail.

        I disagree. Off to the circular file of history with it already.

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  43. Study is flawed, you didn’t gender neutralize the names. all you succeeded in doing was making everyone who was interviewing uncomfortable that they were talking to a boy named sue.

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  46. Pingback: Interviewing.io explores gender in technical interviews – ResoluteDreamer.com

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  49. Pingback: Was Du mit Diversität in Tech zu tun hast • Effektiv Progressiv

  50. Men and women are of course not “identical”… you have a mean I.Q difference of about 5 I.Q points…

    Egalitarism is probably the most powerfull ideology in the West today.

    And if you mask names and voices, you will still have a large mean performance difference between let’s say europeans and africans… no little marxists, not because of “racism and stigmatisation”, just because africans, for genetic reasons, have a smaller brain (on a mean) with a thinner and less circonvoluted cortex, and their mean I.Q is lower.

    Oooh… big surprise… everybody is not identical ??? ^^

    1. Actually the new studies by IQ expert Lynn show women to be outscoring men on IQ test now, soooo…you can scratch that one “Darwin.”

      1. Men and women have the same average IQ, but the spectrum is broader for men, i.e. more idiots and more geniuses. If you’re sampling at the high end, IQ might indicate there are going to be more men. If you believe in IQ …

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