Pretty straight forward IMO. The model is looking for particular qualities in a given resume, and strives to ensure the qualities it looks for is present in resumes it creates. Humans do the exact same thing (unless forced by something like DEI, etc to do otherwise), so I see nothing noteworthy here.
AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights
131–140 of 190 posts
Re: AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights
#132[deleted]
Re: AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights
#133Earlier quoted context omitted.
Same thing happened to my wife as well. I helped her tailor her LinkedIn profile and resume with a lot of attention to detail: adding metrics, keywords, results, etc. Nevertheless, she never received any outreach recruiters and got very few application responses. It went like that for months, almost a year. Then she asked ChatGPT 5.x for help. I was skeptical about the changes it recommended (and was skeptical at all…
> I helped her tailor her LinkedIn profile and resume with a lot of attention to detail: adding metrics, keywords, results, etc. FWIW, when I see a resume with metrics and keywords, I immediately filter it out.
Same.
If it's something like "Refactored the apartment list service improving P99 Latency from 2s to 180ms", it definitely boosts the resumé in my mind. A good engineer would be measuring their impact and likely have numbers like that off the top of their head.
But if it's like "Increased revenue by $18.7M by reducing time-to-first-interaction latency from 2.3s to 117ms, increasing conversion by 47% and LTV by 28%," with the same fidelity on each bullet, I'm very skeptical.
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I don't summarily reject AI-written resumés to be clear, as honestly, it's basically a necessity at this point to be competitive with others; it'd be putting yourself at a severe disadvantage on pure principles in a way that has no real positive net effect on society. Even if you disagree with AI resumé screeners, you're only hurting yourself — especially at a time that has the largest impact on your compensation (i.e. negotiating salary at job start is one of the most valuable ways to spend your time since it will pay you back every paycheck).
Though I _do_ tend to question resumés that look like they were written almost entirely by an LLM without the candidate providing significant context and refinement.
Re: AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights
#134Earlier quoted context omitted.
> those keyword lists often include, in my experience, proficiency in specific tool use This used to be called "buzzword bingo" and was pretty much required. It was how you got past the initial automated filtering step before a human even saw your resume.
I don’t know whether it was ever effective strategy for candidates, but I will simply say that as a hiring manager for over 12 years, I have never been interested in anyone’s resume when I see that.
As someone who's been a hiring manager for around 7 years, I agree with you, but note that the people who screen resumés before they even _get to you_ very well may be looking for those references.
For my own resumé, I include the stack used at each job which I feel strikes a fair balance.
Re: AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights
#135Anecdata, sample size of one: When I was looking for my next role after being laid off, I didn’t get much of a response with my human handmade resume despite my experience Just for kicks, I asked ChatGPT to “Analyze my resume and give it a score for what percentage it was in” then I asked it to revise it to make it score as high as possible I still tweaked and fact checked it but after I started sending that out, I g…
I was recently job hunting and did something similar. Had it check my bullets and see if they "read well" and it suggested many many tweaks. I tried a few. I'm not sure how much more it helped the applications though.
Re: AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights
#136Anecdata, sample size of one: When I was looking for my next role after being laid off, I didn’t get much of a response with my human handmade resume despite my experience Just for kicks, I asked ChatGPT to “Analyze my resume and give it a score for what percentage it was in” then I asked it to revise it to make it score as high as possible I still tweaked and fact checked it but after I started sending that out, I g…
Probably gonna get downvoted for this, but when you give an anecdote you don't have to preface it with "anecdata, n=1 sample size". We know it's from your individual experience because it's a story about your individual experience. We've been doing this for all of human history. This is some kind of strange milieu of trying to always sound scientific, or it's fear of the "well akshually I'm gonna need to see a random…
It became necessary because, for years (decades), if you made a comment online that your personal experience informed you in such-and-such a way, the first comment would always be some moronic comment dismissing that personal experience because it is just one person’s experience. So, to avoid that idiocy, people started to preface their anecdotes by acknowledging that they know it is an anecdote. It sets the tone for the conversation.
Re: AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights
#137Earlier quoted context omitted.
What counts as a keyword here? If you're hiring for a frontend developer and you see e.g. "Redux" do you just can it?
Knowing or having experience with Redux isn’t going to cause me to pick you over someone else who doesn’t list it for a job where I’m paying you hundreds of thousands of dollars. I look at other skills. I would not can it in isolation, but if I see a comma-separated list like: “proficient in redux, react, html, JavaScript, sql, kubernetes, word and excel”… then yes, you don’t make the cut. Or if you list your Microso…
What would be an example of a positive signal?
Re: AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights
#138Earlier quoted context omitted.
> I helped her tailor her LinkedIn profile and resume with a lot of attention to detail: adding metrics, keywords, results, etc. FWIW, when I see a resume with metrics and keywords, I immediately filter it out.
Same. If it's something like "Refactored the apartment list service improving P99 Latency from 2s to 180ms", it definitely boosts the resumé in my mind. A good engineer would be measuring their impact and likely have numbers like that off the top of their head. But if it's like "Increased revenue by $18.7M by reducing time-to-first-interaction latency from 2.3s to 117ms, increasing conversion by 47% and LTV by 28%,"…
I wish it was at least normalized to submit two resumes - one for AI and one for humans. Threading the needle to please both audiences is such a crap-shoot.
Re: AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights
#139Further, LLMs consistently think LLM written content is "good".
Ask an LLM to write some design doc for you, wait until you get one that's very bad, send it to other LLMs and get their feedback, they will typically have good things to say.
Compare that to a very well written document you have. They will typically have a lot more bad things to say, even if the premise is solid.
Someone should study this.
LLMs clearly have a lot of value. But IMO this is very interesting and points out a weakness that's not entirely clear what the full ramifications of it are.
I suspect LLMs also have a major bias to code they write.
Take something universally considered to be well written like Redis, feed it to an LLM for feedback. They'll probably find much to pick apart (and a lot of it may be flat out wrong).
Feed the same LLM some clearly garbage LLM repository. Do they have a similar response as they do with design? Do they treat language different than code, and they're just susceptible to the way they write regular language that's different from logical code? Or do they have the same problem?
Has anyone done this?
Re: AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights
#140Earlier quoted context omitted.
Same thing happened to my wife as well. I helped her tailor her LinkedIn profile and resume with a lot of attention to detail: adding metrics, keywords, results, etc. Nevertheless, she never received any outreach recruiters and got very few application responses. It went like that for months, almost a year. Then she asked ChatGPT 5.x for help. I was skeptical about the changes it recommended (and was skeptical at all…
Kafkaesque
The Orwellian nightmare is nigh