Job Applicant Resumes Are Effectively Impossible to De-Gender
unite.ai
unite.ai
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
Specifically, we perform 1-1 matching without replacement such that for each male resume, we find a female resume that is within 2 years of experience, has the same degree, field of study, and has a resume similarity score (i.e. cosine similarity of resume vector representations) of at least 0.7.
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.)
A lot of machine learning sounds like the computers are just cargo-culting. But this concrete example is spectacularly so.
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.
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.
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.
You also need to consider the risks and circumstances, sometimes you want every tiny edge possible.
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 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.
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.
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.
That's precisely what ML is. Though to be fair, interviewing done by real humans is also largely based on cargo culting.
recruiting a woman also increases my managers KPI.
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.
To be clear I disagree with calling it hate also if it goes the other way.
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.
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.
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.
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.
I have played the poverty card before regarding student loans.
I struggle to believe what I'm reading.
I'm just curious what people of future will think about this.
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.
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".
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.
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.
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?
This is exactly the line of thinking that causes the aforementioned reluctance to hire yet another white male.
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?
> 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...
The HN crowd might like Lauren Klein's approach to this via data science [1] - I do.
I'm also sure a woman has a far better chance of being hired into tech than a man with the same qualifications.
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.
Also:
https://statmodeling.stat.columbia.edu/2019/05/11/did-blind-...
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."
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.
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.
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.
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.
Can we train ML to recognise them from a CV? Do they revognise each-other?
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.