Job Applicant Resumes Are Effectively Impossible to De-Gender(unite.ai)
unite.ai
Job Applicant Resumes Are Effectively Impossible to De-Gender
https://www.unite.ai/job-applicant-resumes-are-effectively-impossible-to-de-gender-ai-researchers-find/
107 comments
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> The model had learned through historical hiring data that men were more likely to be hired, and therefore rated male resumes higher than female resumes.
A lot of machine learning sounds like the computers are just cargo-culting. But this concrete example is spectacularly so.
A lot of machine learning sounds like the computers are just cargo-culting. But this concrete example is spectacularly so.
Well, it's not the computers faults that the people designing the system failed to (and probably are completely unable to) provide a proper measure of "value once hired". In the absence of a signal to tell you that you're stuck looking for proxies, and that it then ends up doing the same as many humans and assume that "past success at getting hired" is a good proxy for "value once hired" is not very surprising.
The machine learning is "cargo-culting" in many instances because humans are cargo-culting and thus are creating models that repeat the same motions rather than try to provide qualitatively better data.
But the interesting thing is that in doing so it does reveal a lot of things about human decision making that may help inform improvements.
The machine learning is "cargo-culting" in many instances because humans are cargo-culting and thus are creating models that repeat the same motions rather than try to provide qualitatively better data.
But the interesting thing is that in doing so it does reveal a lot of things about human decision making that may help inform improvements.
If one gender really did provide more 'value once hired', would it be reasonable for the AI to favour them?
Not necesarily, because it could have given more value for the wrong reasons.
Imagine a misoginistic, but horny, boss, that only hires good working ugly men and hot dumb women, specifically looking for these traits in order to have a "playing" field. If the AI got in here it would be constrained to the same parameters, af if it was looking for good workers of any gender it would prefer men.
An the thing is that although this boss is part of history (we hope) the influence they had on the workforce at large was enormous, and it'll be a long time before this is reversed.
And this without taking into account all the coworkers that could hinder your job because you are not part of their cadre (gender, football club, fortnite clan, whatever).
We really should exepriment with equality, or even the opossite extreme, before considering one gender "superior" than another for a given role. And even then there would be exceptions.
Imagine a misoginistic, but horny, boss, that only hires good working ugly men and hot dumb women, specifically looking for these traits in order to have a "playing" field. If the AI got in here it would be constrained to the same parameters, af if it was looking for good workers of any gender it would prefer men.
An the thing is that although this boss is part of history (we hope) the influence they had on the workforce at large was enormous, and it'll be a long time before this is reversed.
And this without taking into account all the coworkers that could hinder your job because you are not part of their cadre (gender, football club, fortnite clan, whatever).
We really should exepriment with equality, or even the opossite extreme, before considering one gender "superior" than another for a given role. And even then there would be exceptions.
How much more value?
Suppose men were providing $10 in additional value over the time they work for you. That's barely even worth a Starbucks gift card, the sort of thing you probably give away to people who guess the number of marbles in a jar at a company mandatory fun event. You definitely shouldn't hire James rather than Deborah because of an average $10 gender difference unless they are literally interchangeable. If their sole distinguishing feature was somehow that Deborah's smile was nicer, or James' laugh was more annoying, that's probably still enough difference to out-weigh this $10 lifetime value. And the reality is your candidates just aren't that similar. Deborah's six years experience in a similar role while James is applying with no relevant background should not lead to the conclusion that all that matters is the average "value once hired" for James will be higher based on gender averages.
Now, suppose women are providing $10M in additional value. Well, I think we'd have noticed something that dramatic by now right? "Gee, my start-up with five women broke even in the first six weeks because of how awesome women are". So we can rule that sort of large figure out as implausible.
I think a discussion about whether it's reasonable needs to take the magnitude of the difference into consideration, which would first mean (if you wanted to do this) finding out for sure how large that difference even is. My guess is, if you did this, you get a very small answer and so then the discussion is really easy: No.
But before you could do that, you'd need to have a decent objective measurement of this "value once hired", and that part is far harder so it won't get done.
Suppose men were providing $10 in additional value over the time they work for you. That's barely even worth a Starbucks gift card, the sort of thing you probably give away to people who guess the number of marbles in a jar at a company mandatory fun event. You definitely shouldn't hire James rather than Deborah because of an average $10 gender difference unless they are literally interchangeable. If their sole distinguishing feature was somehow that Deborah's smile was nicer, or James' laugh was more annoying, that's probably still enough difference to out-weigh this $10 lifetime value. And the reality is your candidates just aren't that similar. Deborah's six years experience in a similar role while James is applying with no relevant background should not lead to the conclusion that all that matters is the average "value once hired" for James will be higher based on gender averages.
Now, suppose women are providing $10M in additional value. Well, I think we'd have noticed something that dramatic by now right? "Gee, my start-up with five women broke even in the first six weeks because of how awesome women are". So we can rule that sort of large figure out as implausible.
I think a discussion about whether it's reasonable needs to take the magnitude of the difference into consideration, which would first mean (if you wanted to do this) finding out for sure how large that difference even is. My guess is, if you did this, you get a very small answer and so then the discussion is really easy: No.
But before you could do that, you'd need to have a decent objective measurement of this "value once hired", and that part is far harder so it won't get done.
If you are in a gunfight, would you rather have a slight advantage?
You also need to consider the risks and circumstances, sometimes you want every tiny edge possible.
You also need to consider the risks and circumstances, sometimes you want every tiny edge possible.
The problem with this is that it presumes you know which actually has an advantage in that scenario to within the error margins.
In the $10 example, I'd argue chances are very small that you actually know who provides the most value because just a tiny shift in the measurement criteria could shift things in the other direction.
If such a tiny edge matters, it's an indicator you have a problem.
In the $10 example, I'd argue chances are very small that you actually know who provides the most value because just a tiny shift in the measurement criteria could shift things in the other direction.
If such a tiny edge matters, it's an indicator you have a problem.
Not to mention someone’s value ends up varying based on what they are assigned to and who they work with. Frankly the whole idea of precisely measuring individual value is bean-counting bullshit.
Sure, you spend as long as you like figuring out how best to achieve the slight advantage, meanwhile I shoot you in the head.
Analysis paralysis, figuring out what to do costs time and time is expensive so you should avoid this unless you've got a good reason to expect it actually matters and you believe you can actually do it correctly.
Analysis paralysis, figuring out what to do costs time and time is expensive so you should avoid this unless you've got a good reason to expect it actually matters and you believe you can actually do it correctly.
In this instance most societies have decided that discrimination by gender is a negative for society irrespective of whether or not you can come up with a metric that gives one gender a higher value to the company, so no.
But it would still be important to understand how the AI would assign value because it would matter to ensure that it does not apply criteria that are not legal.
At the same time, what this seems to suggest is also that purely judging by other factors in resumes might effectively act as a proxy for directly judging by gender. As such the more interesting question to me is whether that means that deciding based on such factors might put employers at risk of claims of gender discrimination.
But it would still be important to understand how the AI would assign value because it would matter to ensure that it does not apply criteria that are not legal.
At the same time, what this seems to suggest is also that purely judging by other factors in resumes might effectively act as a proxy for directly judging by gender. As such the more interesting question to me is whether that means that deciding based on such factors might put employers at risk of claims of gender discrimination.
Yes, because of the AI is picking my brain surgeon, I want the best person, and I’m absolutely opposed to any equity, just raw talent and merit when it comes to someone operating on my brain again.
the AI is just a product of it's inputs, how it's used, and whether it violates discrimination laws, are totally up to the users.
Sort of, but we have to be careful not the wash the hands of people all too willing to discard responsibility. It's correct to say "it's just statistics on data" but it also provides cover to the processes that use it. "How can you be against hiring the best person for the job" the argument goes, completely ignoring that the "best" isn't scientifically defined, and includes the same sexist biases we were trying to get rid of.
I read that as specifically saying that the ethical and legal responsibility is entirely on the people. (i.e. I think you're agreeing with the comment you're replying to)
Yes, sorry for the lack of clarity. Users shouldn't blame the machine when they control every aspect of it and how it's used. It's on you as a user to understand your tools and data.
The there are differences in human learning and knowledge and machine learning, one being the kind of errors machines makes compared to humans. A human is unlikely to confuse the side of a truck with the sky (tesla), or the image of an item with the actually item. Humans in turn get effected by stress, metabolism and other biological factors.
For an AI to actually predict which employees are going to benefit the company, it need to understand both how the company has evolved over the data period, how it will likely continue to grow, and that some employees that have successful careers are not necessary the people that contribute to the success of the company. An AI might think nepotism is a great indicator for a highly retained and employable employee, but a human would recognize it for what it is but may accept it as an acceptable level of corruption. It would be the same conclusion (ie, hire the person), but for two very different reasons.
For an AI to actually predict which employees are going to benefit the company, it need to understand both how the company has evolved over the data period, how it will likely continue to grow, and that some employees that have successful careers are not necessary the people that contribute to the success of the company. An AI might think nepotism is a great indicator for a highly retained and employable employee, but a human would recognize it for what it is but may accept it as an acceptable level of corruption. It would be the same conclusion (ie, hire the person), but for two very different reasons.
> A lot of machine learning sounds like the computers are just cargo-culting.
That's precisely what ML is. Though to be fair, interviewing done by real humans is also largely based on cargo culting.
That's precisely what ML is. Though to be fair, interviewing done by real humans is also largely based on cargo culting.
I would call it "pattern recognition" rather than "cargo culting". Cargo culting is an example where pattern recognition fails.
I mean. This seems like a good example of pattern recognition probably failing?
That depends on how you measure it. It accurately reproduced how previous recruiters worked, but this approach might not be the best. The thing here is that the metric "the best candidate" is very hard to find, so people use the metric "candidate that were previously recruited". I would call it an instance of the "streelight effect": https://en.wikipedia.org/wiki/Streetlight_effect
I think the key insight is that the humans the AI replaces were probably also cargo culting. They'd just get a bit more defensive if you suggested that.
Have you ever tried to tell an ML engineer that their model is biased (or even worse sexist). I think the defensiveness is about equal.
FWIW at least at the company I work this gives women an advantage. I get a nice bonus for anyone I recruit but recruiting a woman also increases my managers KPI.
We have quotas we need to fill. Hiring a white male is basically a nonstarter without prostrating yourself before HR
This reads to me as it's okay to discriminate as long as it's for the "right group".
I honestly feel a bit sick about all this. Maybe it's the only way, but it doesn't feel like it is from my perspective.
I honestly feel a bit sick about all this. Maybe it's the only way, but it doesn't feel like it is from my perspective.
It is absolutely just another form of discrimination. In the past people thought they had reason too.
If you want compensatory justice and you are male, the only thing you could do is to resign and give your job to a woman. If you demand the same from others you are just as bad as someone that discriminated in the past.
There is no higher motivation behind this. HR just follows laws though, so you should boot your legislators for this.
Additionally it very much influences work relations for women far more negatively than for men, so they don't even profit from this aside very few selected high earning positions. But those will still have a problem with authority.
If you want compensatory justice and you are male, the only thing you could do is to resign and give your job to a woman. If you demand the same from others you are just as bad as someone that discriminated in the past.
There is no higher motivation behind this. HR just follows laws though, so you should boot your legislators for this.
Additionally it very much influences work relations for women far more negatively than for men, so they don't even profit from this aside very few selected high earning positions. But those will still have a problem with authority.
That sucks to hear as a white male but I still put the responsibility on myself. I should be so good at my job that they don't care that I'm a white male.
The context here is that applicants are overwhelmingly white and male. Most of the time it won't matter because you're the only category in the pool.
Depending on what country you are in, this is illegal no? Not hiring someone based on their ethnicity who is otherwise qualified/the best candidate is racial discrimination, regardless of what that ethnicity is.
In Brazil this is illegal, but when I saw this happening and told the government about it, I got an e-mail from a female judge, saying that hiring only women is fine because it "corrects the distortions", despite it being against the constitution... This is anecdote but I can say from personal experience that companies can get away with it.
I was horrified to find this [0] written in law for my country:
> Appropriate measures aimed at achieving true equality are not regarded as discriminatory.
[0] https://www.fedlex.admin.ch/eli/cc/1996/1498_1498_1498/en#ar...
> Appropriate measures aimed at achieving true equality are not regarded as discriminatory.
[0] https://www.fedlex.admin.ch/eli/cc/1996/1498_1498_1498/en#ar...
I have a white male friend who works in academia in NYC. They had a staff meeting at the college about hiring for open positions in several departments and were explicitly told that they were looking only for "diverse" candidates and that white men would not be considered.
We happily take anyone qualified and I get my bonus. But as mentioned, percentage of female employees is a KPI and it makes my boss looks good and it makes the CEO happy in all hands meetings.
I am an engineer that involves in hiring (technical interviews, assignment reading (we have a home assignment for candidates)) and I have a couple experiences in which I felt like my decision was overruled.
One was a female applicant. I was reading her assignment and felt like it was subpar and my decision was a thumbs down. Later we went forward with the application to the technical interviews because it was a female and we "really needed to hire one" (to be clear, I didn't even know it was a female until my decision is overruled. We don't receive CVs or names while reviewing home assignments)
The other was a minority from a different country. My decision was thumbs down because the applicant basically lied in his CV about his past experience. HR overruled that by saying something like "...in his culture it is hard for people to admit their weaknesses..." and to top it off "we don't have anyone in our company from country X!" and he got hired.
So yea, positive reinforcement means you are not really looking for qualification and most things can be ignored to fill some quotas. And you won't be getting that bonus when someone else refers a minority that gets hired.
One was a female applicant. I was reading her assignment and felt like it was subpar and my decision was a thumbs down. Later we went forward with the application to the technical interviews because it was a female and we "really needed to hire one" (to be clear, I didn't even know it was a female until my decision is overruled. We don't receive CVs or names while reviewing home assignments)
The other was a minority from a different country. My decision was thumbs down because the applicant basically lied in his CV about his past experience. HR overruled that by saying something like "...in his culture it is hard for people to admit their weaknesses..." and to top it off "we don't have anyone in our company from country X!" and he got hired.
So yea, positive reinforcement means you are not really looking for qualification and most things can be ignored to fill some quotas. And you won't be getting that bonus when someone else refers a minority that gets hired.
And to add, when I was hired I was also "first employee from country Y". Still wondering if I was a diversity hire
It is crappy to think you got a job not because you deserved it/good at it but yeah... Well if you need the opportunity I guess that's nice.
I have played the poverty card before regarding student loans.
I have played the poverty card before regarding student loans.
My properties ensure that I won't face quotas that would be a barrier for emplyoment (well maybe my gender, I'm male), so, I'm just curious, so what if there's one position to fill, the male candidate scores 60%, and the female one scores 58% (using my hypothetical ranking system which actually works and is fair, etc, etc), so just slightly lower. Would your company pick the female candidate in this case?
I guess answering this question "the wrong way" would get a lot of butthurt people showing up to this thread, so feel free to not answer.
I guess answering this question "the wrong way" would get a lot of butthurt people showing up to this thread, so feel free to not answer.
Not OP, but I've seen some of this (though not terribly often). I don't think I've ever seen it come to a head and be explicitly about an attribute like that. I think that would make a lot of people uncomfortable.
Usually what I see is faster movement through the recruiting pipeline. In your case, the woman scores 58% a week before the man takes the test. There's no overt pressure to just accept her score and move on. She's just here, qualified, and ready to save us from having to do more interviews haha.
I don't have a poignant take on whether that's a good or bad thing. It definitely feels better than how I imagine having to pick between the two would, thought it still feels discriminatory. Then again, diversity in some sectors is low, so perhaps it makes sense for the hiring pipeline to give them some form of advantage, and this might be the least bad option.
The whole thing is a mess of moral ambiguity.
Usually what I see is faster movement through the recruiting pipeline. In your case, the woman scores 58% a week before the man takes the test. There's no overt pressure to just accept her score and move on. She's just here, qualified, and ready to save us from having to do more interviews haha.
I don't have a poignant take on whether that's a good or bad thing. It definitely feels better than how I imagine having to pick between the two would, thought it still feels discriminatory. Then again, diversity in some sectors is low, so perhaps it makes sense for the hiring pipeline to give them some form of advantage, and this might be the least bad option.
The whole thing is a mess of moral ambiguity.
People need to realize is actual support for diversity means internalizing there is a benefit.
So if you actually believe there is a benefit to having a team of men and women (I do), then it's not "wrong" to let that guide you to the woman when any score above a 50% indicates someone who will be able to actually do the work.
So if you actually believe there is a benefit to having a team of men and women (I do), then it's not "wrong" to let that guide you to the woman when any score above a 50% indicates someone who will be able to actually do the work.
You're pushing shit uphill with that argument on HN. A lot of these guys have never worked on a team with anything resembling gender balance, and they have an irrational fear of women.
>Hiring a white male is basically a nonstarter without prostrating yourself before HR
I struggle to believe what I'm reading.
I'm just curious what people of future will think about this.
I struggle to believe what I'm reading.
I'm just curious what people of future will think about this.
In most companies hiring anyone requires some form of prostration to HR, so a little extra work to hire a white male is not going to slow anyone down.
A friend of mine works in a large organization in which the head passed down a rule that only women would be hired for positions 'unless' the only available qualified candidate happened to be male.
Can you guess what happened? It hasn't increased the relative number of women hired but now everyone thinks the men are extra special.
A friend of mine works in a large organization in which the head passed down a rule that only women would be hired for positions 'unless' the only available qualified candidate happened to be male.
Can you guess what happened? It hasn't increased the relative number of women hired but now everyone thinks the men are extra special.
The thing kinda is, that it's not only male/female thing, but race's also involved.
Biases biases and biases.
I think we should try to reach equal opportunity by providing access to edu, mentors maybe, an ability to prove themselves, not this kind of "discrimination".
Biases biases and biases.
I think we should try to reach equal opportunity by providing access to edu, mentors maybe, an ability to prove themselves, not this kind of "discrimination".
Exactly, it sets anyone up to failure that would only slightly be under suspicion to have profited from these decisions. Not only by judgement from others, it also undermines the faith of the candidates themselves.
This isn't something to claim without elaborating.
You're saying your company won't hire white males without extenuating circumstances?
I'd love to see the incredibly diverse teams there then... or is this a case of your company used to not hire non-whites and now they're trying to play catch up so they can beat a drum about DNI, meanwhile the culture that lead to a lack of diversity marches on internally?
You're saying your company won't hire white males without extenuating circumstances?
I'd love to see the incredibly diverse teams there then... or is this a case of your company used to not hire non-whites and now they're trying to play catch up so they can beat a drum about DNI, meanwhile the culture that lead to a lack of diversity marches on internally?
> I’d love to see the incredibly diverse teams there then…
This is exactly the line of thinking that causes the aforementioned reluctance to hire yet another white male.
This is exactly the line of thinking that causes the aforementioned reluctance to hire yet another white male.
What on earth are you talking about?
You think the reason they're so desperate they're willing to gaslight minorities over their abilities (according to this anonymous unelaborated source) is because they have diverse teams?
Did you just stop reading after the ellipses or what?
You think the reason they're so desperate they're willing to gaslight minorities over their abilities (according to this anonymous unelaborated source) is because they have diverse teams?
Did you just stop reading after the ellipses or what?
recruiting a woman also increases my managers KPI.
Where I live it is very, very common to see job offers that only take disabled people because the company gets $$$ from the government (this is, from everybody of us, including those ostracised by those policies) when they hire a disabled person.
That's literally the worst.
No, compared to what I hear from other places there is no hate against men compared to what sometimes get described here; it is just that women are preferred and get slightly higher pay on average if my contacts in management are to be believed, but keeping women on board is a priority and also they pay us men very fairly.
No hate - that's an interesting perspective. Such practices, or even just results without intention, would for sure be described as hate (or something negative) if men were preferred and get slightly higher pay.
Don't get me wrong, I'm not calling you out, you just provided description without judgment and that's great. It's just a reflection on how those topics are usually framed, and that's a sad state of affairs.
Don't get me wrong, I'm not calling you out, you just provided description without judgment and that's great. It's just a reflection on how those topics are usually framed, and that's a sad state of affairs.
> Such practices, or even just results without intention, would for sure be described as hate (or something negative) if men were preferred and get slightly higher pay.
To be clear I disagree with calling it hate also if it goes the other way.
To be clear I disagree with calling it hate also if it goes the other way.
"Positive" discrimination is the worst, and makes it pretty clear to everyone on the floor, that the person has been put where he/she is for reasons that have nothing to do with how he/she performs at work. I've seen it first hand, and it was quite embarrassing to watch a female member appointed to a specific committee just for the sake of having a women, that no one listened to, that clearly had no business there, etc...
People deciding what women employment rate and where, should be, reminds me a lot of certain communist bureaucrats deciding what the GDP of the country should be and passing a law saying "this year, the GDP will be x"... you just cannot fake reality.
[deleted]
It's bit sad that there are no examples of processed CVs, especially those where gender was masked by removing some words. I wonder how the NLP models did their job and I am feeling there is NLP looking for tanks using clouds instead (see https://www.gwern.net/Tanks).
I'm not sure gender inequality can be solved by some magic machine making gender disappear. Rather we can progress by making inequality visible and challenging it: only when attitudes change we will slowly improve.
The HN crowd might like Lauren Klein's approach to this via data science [1] - I do.
1: https://data-feminism.mitpress.mit.edu/
The HN crowd might like Lauren Klein's approach to this via data science [1] - I do.
1: https://data-feminism.mitpress.mit.edu/
Thank you, it seems to be an incredible resource, really appreciate you sharing this.
In my native language every verb has a masculine or feminine ending depending on the subject's gender.
I'm also sure a woman has a far better chance of being hired into tech than a man with the same qualifications.
I'm also sure a woman has a far better chance of being hired into tech than a man with the same qualifications.
To be fair, getting hired in tech is still pretty easy in most cases and countries. My country puts way too much weight on formal qualification, but in the current economy you find work nearly everywhere.
On the importance of de-gendering the recruitment process, I have a parallel to submit with a discipline I know well, the professional classical music industry.
A few decades ago, it was commonly thought that the brain of women couldn't understand music. "Women have other preferences," "Their brains are not made for that." If it reminds you of some rhetoric seen here and there, trust your instincts.
Here is what happened in the 70s, using extracts from the book "Blindspot":
"In 1970, fewer than 10 percent of the instrumentalists in America's major symphony orchestras were women, and women made up less than 20 percent of new hires."
"Starting in the 1970s, several major American symphony orchestras experimented with a new procedure that involved interposing a screen between the auditioning instrumentalists and the committee, leaving the applicants audible but not visible to the judges."
"The next twenty years provided interesting evidence. After the adoption of blind auditions, the proportion of women hired by major symphony orchestras doubled—from 20 percent to 40 percent."
Fun fact: those blind auditions didn't start because of gender ethics; they began because the classical music industry was rife with clannishness and nepotism at all levels, which gave the incitive for orchestras to limit the impact from influential professors.
A few decades ago, it was commonly thought that the brain of women couldn't understand music. "Women have other preferences," "Their brains are not made for that." If it reminds you of some rhetoric seen here and there, trust your instincts.
Here is what happened in the 70s, using extracts from the book "Blindspot":
"In 1970, fewer than 10 percent of the instrumentalists in America's major symphony orchestras were women, and women made up less than 20 percent of new hires."
"Starting in the 1970s, several major American symphony orchestras experimented with a new procedure that involved interposing a screen between the auditioning instrumentalists and the committee, leaving the applicants audible but not visible to the judges."
"The next twenty years provided interesting evidence. After the adoption of blind auditions, the proportion of women hired by major symphony orchestras doubled—from 20 percent to 40 percent."
Fun fact: those blind auditions didn't start because of gender ethics; they began because the classical music industry was rife with clannishness and nepotism at all levels, which gave the incitive for orchestras to limit the impact from influential professors.
But that does not mean we have the same situation here. We are living 50 years later and the pipeline for IT looks different than the pipeline for music.
Also:
https://statmodeling.stat.columbia.edu/2019/05/11/did-blind-...
Also:
https://statmodeling.stat.columbia.edu/2019/05/11/did-blind-...
Thank you, you are totally correct; we are living 50 years after, and those pipelines are different. I tried, without great success, to create perspective in a very one-sided thread.
This link is interesting indeed. While there was some fair criticism of this study, I do agree with the conclusion:
"I agree that blind auditions can make sense—even if they do not have the large effects claimed in that 2000 paper, or indeed even if they have no aggregate relative effects on men and women at all."
This link is interesting indeed. While there was some fair criticism of this study, I do agree with the conclusion:
"I agree that blind auditions can make sense—even if they do not have the large effects claimed in that 2000 paper, or indeed even if they have no aggregate relative effects on men and women at all."
Link to the actual paper here: https://arxiv.org/pdf/2112.08910.pdf
Edit after having read it: It's short and easy to read, I would suggest reading it instead of the article linked. I think that more work should be needed before making suggestions. For example, is there a correlation between the "number of words" and the gendering of a resume? Maybe something like "I was the manager of the soap team for 3 years, during which sales increased by 25.6%" compared to "2012-2015: soap team manager". Though this convey less informations. Maybe a KPI box for each job?
Another thing that I would have liked would be to compare that to what actual recruiters can do. If a simple model can do 0.75 and a regular recruiter 0.53, it's not the same as if a recruiter does 0.92.
Lastly, the "list of gender indicating words" seems really small. Just 26 words for 348k resume.
Edit after having read it: It's short and easy to read, I would suggest reading it instead of the article linked. I think that more work should be needed before making suggestions. For example, is there a correlation between the "number of words" and the gendering of a resume? Maybe something like "I was the manager of the soap team for 3 years, during which sales increased by 25.6%" compared to "2012-2015: soap team manager". Though this convey less informations. Maybe a KPI box for each job?
Another thing that I would have liked would be to compare that to what actual recruiters can do. If a simple model can do 0.75 and a regular recruiter 0.53, it's not the same as if a recruiter does 0.92.
Lastly, the "list of gender indicating words" seems really small. Just 26 words for 348k resume.
The obsession with equality (of outcome) is a blight upon our world.
What about this is promoting equality of outcome? The paper is investigating whether information preventing equality of opportunity can be successfully obfuscated.
My boss recently transfered to my team a lady that was lacking very basic skills. I was, and still am happy to mentor her, and have her in our team, like everyone else who joins us. But I suspect that the only reason we keep her is my manager's KPI that is driven by the (well known) views of higher managent in our company. This makes me feel very bad towards all those candidates I rejected that had much more experience than her, but still not enough for the roles we are recruiting for (we set the bar pretty high). This favoritism is implicitly discrimination against these candidates. But you don't fix discrimination with more discrimination. Instead of moving forward, we repeat the same mistakes with different decoration. I want to move somewhere far away :(
I assume this controlled for the fact the genders don't apply to certain roles at the same rate? I mean I assume most people reading this right now are men simply because this site is a business/tech forum and men tend to have more of an interest in these two things.
Similarly, if I got an empty job application for a rust developer role I would expect 90%+ of time it's a man applying. If it were a UI/UX designer I'd be less confident. Maybe a 50/50 split based on my experience. Were it a social media manager role, I'd then assume it was a woman.
Similarly, if I got an empty job application for a rust developer role I would expect 90%+ of time it's a man applying. If it were a UI/UX designer I'd be less confident. Maybe a 50/50 split based on my experience. Were it a social media manager role, I'd then assume it was a woman.
The article says
Also "with a margin-of-error of 2 years, in terms of experience in their field" is a quite a margin for junior-mid positions.
Male and female samples were matched 1-1, and a subset obtained by pairing up the best objectively job-appropriate male and female candidates, with a margin-of-error of 2 years, in terms of experience in their field. Thus the dataset consists of 174,000 male and 174,000 female résumés.
Also "with a margin-of-error of 2 years, in terms of experience in their field" is a quite a margin for junior-mid positions.
Reading the conclusion of the article (not the paper), it sounds like the difference in writing between genders seem to be too strong for current techniques to work.
I wonder what happens if you work with someone of the opposite sex to prepare your resume.
Yes!
Or use the AI to identify the gender indicating words of the opposite gender that are synonyms of words you've used and swap those in - might be a start (although deeper things like sentence structure could still influence the classification)
I'd say the current techniques work too well :)
Also it says something about the current wave of "AI" being able only to mimic the human thought process and not being able to come up with anything different. The I in AI is still out of reach.
Also it says something about the current wave of "AI" being able only to mimic the human thought process and not being able to come up with anything different. The I in AI is still out of reach.
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But you are not trying to de-gender it for AI’s right? It’s humans that still have to distinguish.
What if we take something like GPT-3 and have it rewrite all the resumes before they sent to processing into NLP models?
If we grant as correvt the reseatch saying upper eshelons of society is filled with sociopaths and psychopaths, https://www.forbes.com/sites/victorlipman/2013/04/25/the-dis...
Can we train ML to recognise them from a CV? Do they revognise each-other?
Can we train ML to recognise them from a CV? Do they revognise each-other?
Automation should be removed from the hiring process.
But to which level? Basic automation, i.e. sorting by proficency is not really done better by a human than machine. The problem doesn't lie in automation but how we apply it. Don't blame the tool, blame the user.
I think there is a lot of room for removing overautomation and creating transparency. Though just outright abandoning automation would hurt even more since human routines are even harder to evaluate/ make transparent.
I think there is a lot of room for removing overautomation and creating transparency. Though just outright abandoning automation would hurt even more since human routines are even harder to evaluate/ make transparent.
If you have too many CV’s to look at, you can simply trash a random set (or automatically send a rejection email), then look at the remainder. You should still have roughly the same ratio of skilled candidates.
To still have the same ratio of skilled candidates would mean the automatic rejection has zero merit. For example, what about filtering for a minimum of x years of work experience for a senior position? Surely your ratio of skilled and appropriate candidates will increase.
What kind of proficiency do you mean, exactly? To me, the concept sounds too nebulous for a machine to analyse, as it needs precise instructions/criteria.
How do you "sort by proficency"?
icare_1er(2)
The entire premise of the research relies on the fact that the resume samples were well matched: in other words, the quality of male candidates was equal to that of female counterparts. In the paper, it says
I am not sure if this method is sufficient to create a match dataset. In other words, it is entirely possible that the (legitimate) experience or quality differences in resumes may act as the signal for the ML algorithm.
It would be interesting to see descriptive analytics on male and female applicants. IIRC men and women have differences in self-selection for precisely things like this. It is plausible (or even probable) that such difference in self-selection crept into the dataset despite the research's resume matching process.
(As a clarification, this is a critique on the research methodology, and I am not claiming one gender is less qualified than the other in general.)