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AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights

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Re: AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights

#151

Post by p_stuart82 »

charliebwrites wrote: post #15

Anecdata, 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…

that's the loop though. if GPT does the screening, people learn to write for GPT. once that loop exists, why would the company selling the filter want it gone?

Re: AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights

#152

Post by cyberax »

aDyslecticCrow wrote: post #93
visarga wrote: post #70

When classifying resumes it is better to use the LLM as a feature extractor, think of 10-20 features you base your decision on, and extract them by LLM. The LLM only needs to do lower level task of question answering. Then you fit a classical ML model (xgboost for example) on the extracted features, based on company triage data points. This way you don't rely on the biases in the model, you can decide what criteria t…

I'd rather my employers just does the classic of shredding random 80% and looking at the remainder properly.

Ah, the good old "we don't need unlucky losers here" strategem.

Re: AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights

#153

Post by davebren »

Fezzik wrote: post #136
davebren wrote: post #103

Earlier quoted context omitted.

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…

Yeah but we can't let the insufferable dictate our way of speaking. In spoken language I hear it mainly by people that don't have a scientific background trying to sound more scientific.

Re: AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights

#154

Post by mcv »

andsoitis wrote: post #107
hiAndrewQuinn wrote: post #105

Earlier 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…

Unfortunately many recruiters do look at that. I'm always a bit disappointed when someone wants me to rate my Java experience, or complains that my CV doesn't mention REST experience.

Re: AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights

#156

Post by tayo42 »

davebren wrote: post #108
tayo42 wrote: post #86

Earlier quoted context omitted.

Llms were good for being objective and helping cut out stuff from mine. Harder to do when you personally think everything you ever did is important.

It actually is important and if I was hiring you I'd find it useful to get a more comprehensive understanding of your experience, especially if there's something I'm aware is a very challenging problem to solve. And it would provide more things to cross-examine in interviews to make sure it's not fake. The idea that people hiring are saving time by not reading an extra resume page when deciding on someone that will h…

If I could give two resumes one for the recruiter and one for the hm I would.

But as an applicant, I'm dealing with recruiters who think Java and Javascript are basically the same.

Re: AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights

#158

Post by mcv »

andsoitis wrote: post #106
mikeyouse wrote: post #102

Earlier quoted context omitted.

Which is a very “HN” sentiment when the vast majority of recruiters and hiring managers are absolutely not doing the same. Especially for roles outside of tech.

Yeah I don’t know what others are doing, but I work in the valley and those elements signal checklist mentality. To wit, those keyword lists often include, in my experience, proficiency in specific tool use, rather than communicating skills that transcend tools, which tells me the person is likely not very dynamic or creative.

I rewrote my resume in a way that sounds like exactly what you want: focus on skills that transcend tools instead of just the tools, and every recruiter asks me about tools.

Re: AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights

#159

Post by dolebirchwood »

jwolfe wrote: post #142
schrodinger wrote: post #133

Earlier quoted context omitted.

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%,"…

> 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%," wi…

Because the latter's "business impact" is clearly made-up bull shit?

Re: AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights

#160

Post by drillsteps5 »

yagi0x00 wrote: post #78
drillsteps5 wrote: post #71

That's what people on both side have been doing for at least couple years already. Recruiters scan resumes for the best match with LLMs, candidates use the same LLMs (there's only like 3 of them) to tweak their resume for better match. I don't know what research you need to see why that makes sense.

This indicates that resumes created by the same model may have an advantage over those created by other model, so I suppose technically you may have a small advantage if an insider tells you the resume parsing tool is powered by Gemini as opposed to the other models. My broader discomfort is that we are still learning about model biases while human biases are arguably better understood, and I don't like the ethics of…

I wasn't saying that this is the optimal solution (it clearly is not). I was saying that it makes perfect sense for both sides - HR has their work automated and candidates have better chance to be noticed - and therefore became a common practice in many places.

The well has been already poisoned, to survive you have to get in on the action.

Don't want to play this game? Make connections, set up the network, and use it to get/stay employed.

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