an AI resume screener had been trained on CVs of employees already at the firm, giving people extra marks if they listed “baseball” or “basketball” – hobbies that were linked to more successful staff, often men. Those who mentioned “softball” – typically women – were downgraded.
Marginalised groups often “fall through the cracks, because they have different hobbies, they went to different schools”
Of course AI does has bias with casual racism and sexism. It’s been trained on a whole workforce that’s gone through the same.
I’ve gotten calls for jobs I’m way underqualified for with some sneaky tricks, which I’ll hint involves providing a resume that looks normal to human eyes, but when reduced to plaintext essentially regurgitates the job posting in full for a machine to read. Of course I don’t make it past 1 or 2 interviews in such cases but just a tip for my fellow Lemmings going through the bullshit process.
Why are you applying for jobs that you’re not qualified for? Even if you BS your way through the interviews you’ll have to actually do the work.
Buckshot strategy. (I apologize if the use of that term is disrespectful to your username). I applied to hundreds of jobs over the year. Some had intermediate/junior in the position. Some were just at companies I wanted to be at more, even if not that role specifically.
I love how thoughtful you are.
You’ve not looked at job postings in a while, have you?
No one is “qualified” for anything anymore. I’ve literally seen postings with requirements like “8 years experience with [Programming Language]” when said language was only created 3 years ago.
They’re all written by HR drones with zero understanding of the actual needs of the department they’re hiring for.
You have to apply for things you’re unqualified for if you want to get anywhere now.
I actually was on the job market just a few months back for the first time in 15 years. Those sorts of comedy postings are not common. It’s true that often the position doesn’t require as much experience as the “dream candidate” they’re asking for in the job posting, but A) they’re aware of that, and B) they take that into account when screening resumes. Lying on your resume is not required, it’s only going to waste everyone’s time if you do.
“qualified” is a loaded term. Industry or product knowledge go a long way to succeed in quite a few businesses.
As an example “Unqualified” for sales might just mean the applicant doesn’t have an MBA or whatever other degree, even though they have dealt with break fix service and other solution oriented work.
Similarly, if a sales rep went into installation or project management they would have a leg up.
The worst project management I’ve ever seen was done by salespeople, probably because they’re laughably unrealistic about what is actually possible and how fast and how well it can be done, so overpromise all the time thus condemning a project to fail for the start (want to see a guaranteed deathmarch project: go look for any were a salesperson got put in charge), tend to expect that problems get solved with fast talk and change the requirements everytime they speak with customers/stakeholders as if it one could just, say swap the foundations of building half-way done add some more floors on top.
That genuine optimist that comes from not examining something so close and in depth that you start seeing enough detail to spot the potential problems and start grasping the true scope of the task, which is maybe the best quality for selling stuff, is pretty much the worst quality for actually making stuff or lead those who make stuff (in this latter case because of being shit at setting and managing expectations).
Theirs is the last kind of personality you want managing the creating of anything in any way complex.
Yeah absolutely.
The best sales will actually understand their product in depth and will be able to educate their customer on it, though. They also won’t waste their time with unrealistic expectations.
In the area I’m in (software engineering) were there is no product to sell and it’s all tailor made to fit or heavilly adapted solutions, the closest to what you describe are called “consultants” who have a technical background.
My experience with pure sales people trying to manage a project was always pretty bad, maybe because custom software is just too open ended and unique, so lacks the kind of references and past usage history that a good salesperson can use as guidance.
Yeah, I already said what I wanted to the other commentor, but the situations had to do with titles, years of experience, degrees, visas variously. With a bit of training and a lot of effort on my part I could fulfill a role just fine but it could be one level higher than expected paygrade for someone like me.
My interview skills aren’t the best. How I got the job I eventually got was not just more practice but because the questions that were asked of me were actually about what I know of the industry itself, which is something I could just talk and talk and talk about that with them all day if that’s what they wanted.
OP said “Of course I don’t make it past 1 or 2 interviews in such cases.” So it seems pretty straightforward that he wasn’t qualified, as in he wasn’t going to succeed in those roles.
Not making it through the interviews doesn’t indicate job success, it indicates job attainment. I’m saying job success is less related to listed qualifications than you might think.
Step one in succeeding in a job is passing the interviews and getting that job.
OP was just wasting everyone’s time, both his own and the interviewers.
No lol
The job is producing usable work
The interview is getting the opportunity to get paid for producing usable work.
And I’m pretty doubtful that OP would be capable of producing usable work. He says it himself, he’s being deceptive about his abilities.
No that’s your prejudice about what he says.
He doesn’t comment on anything past interviewing. You don’t know his opinion on if he can produce usable work.
You haven’t said anything to rebut my statement that attaining work and completing work are different thing.
Idgaf what you doubt unless you can give some narrative that lends it credibility.
fucking bonkers that institutionalized racism can exist to such a degree that it shows up IN OUR COMPUTERS.
we’re so racist we made the computers discriminatory too.
I don’t think you know how LLM’s are trained then. It can become racist by mistake.
An example is, that there’s 100.000 white people and 50.000 black people in a society. The statistic shows that there has been hired 50% more white people than black. What does this tell you?
Obvious! There’s also 50% more white people to begin with, so black and white people are hired at the same rate! But what does the AI see?
It sees 50% increase in hiring white people. And then it can lean towards doing the same.
You see how this was / is in no way racist, but it ends up as it, as a consequence of something completely different.
TLDR People are still racist though, but it’s not always why the AI is.
The bias is really introduced at the design stage. Designers should be aware of demographic differences and incorporate that into the model to produce something more balanced. It’s far from impossible to design models that do not become biased in this way, even from biased data - although, that is no to say it’s easy.
you are right, i don’t know how LLMs are trained, but ironically, this is a perfect example of a minority being privelaged by a system, and racism is still very much involved.
an important assumption you have to consider: in your example, why did the AI know what race people are in the first place? it seems a small consideration but it’s so wildly significant.
the modern understanding of race was not present throughout all of history, and only arose in the 17th century. without getting into the weeds, the fact that your fictional AI can distinguish between whiteness and non-whiteness already means it was designed by someone who understands those structures, and let them slip into the AI itself.
a perfectly well-meaning and anti-racist designer would prevent the AI from even recognizing race at all costs, both directly by sanitizing training data to remove race from the inputs, and indirectly by noting correlations with other data (such as sports, in this article) and controlling for that.
I suppose it depends on how you define by mistake. Your example is an odd bit of narrowing the dataset, which I would certainly describe as an unintended error in the design. But the original is more pertinent- it wasn’t intended to be sexist (etc). But since it was designed to mimic us, it also copied our bad decisions.
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Oh there is so much racist data that the AI is being trained on.
Your example is a simple one. But there are discriminations based on names for instance, so Johns are hired more than Quachin is, and that is by people, before it gets to the AI.
How do you make something like that?
White text on a white page?
Are you a recruiter or someone making HR software?
No I do qa right now, working to shift back into gameplay programming