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AI Hiring Tools Yield Racial Bias and Systemic Rejection; 26% Black & 15% Asian

https://hai.stanford.edu/news/ai-hiring-tools-can-yield-racial-bias-and-systemic-rejection
56•sizzle•1h ago

Comments

anonfunction•53m ago
This is something I've been working on exposing to AI labs through my startup LatentEvals[1], and found similar results in other industries from lending to insurance claims.

Happy to share some sample reports if anyone is interested!

1. https://www.latentevals.com/

etchalon•43m ago
Don't have much to add beyond being grateful for everyone working to call this out, with a hope some lawsuits drop and our SCOTUS doesn't decide racial bias in AI is fine because we can't prove the AI is racist in its heart.
alain94040•42m ago
The European Union passed The Artificial Intelligence Act, which classifies:

High-risk – AI applications that are expected to pose significant threats to health, safety, or the fundamental rights of persons. Notably, AI systems used in health, education, recruitment, critical infrastructure management, law enforcement or justice. They are subject to quality, transparency, human oversight and safety obligations

That's a pretty common sense legislation to me.

anon373839•25m ago
The AI “safety” industry is lobbying for federal preemption so that states won’t have the power to enact these types of sensible regulations.
asdff•40m ago
Some job application websites I've seen actually have a yes or no option to consent to AI review that they claim is to simply assist HR and not actually screen you. I always select no. There is no way that selecting yes would ever be in my interest. I'm sorry, I'm going to force a real human to look at my stuff if I still can.
bluefirebrand•32m ago
My fear is that pressing "no" on stuff like that is going to become an auto-rejection in the vast majority of cases
simpaticoder•25m ago
It won't be rejected. Your resume will be meticulously placed into a human review queue pending the allocation of someone to look at the contents. Meanwhile the position will be filled, and so serving no purpose the review queue will be emptied.
bluefirebrand•22m ago
Oddly enough, being rejected by process versus being rejected by a person doesn't actually make me feel any better about the coming future

:)

jcims•18m ago
It's probably not going to be an auto-rejection, it's just going to sit in a queue that looks like this

    Screened Applications [13]
    Unscreened Applications [39148]
everyone•38m ago
Its fucking crazy that people are using these systems for important tasks like hiring. They have zero understanding about how these systems work. And LLMs are absolutely not designed to do those sorts of jobs, they're designed to be chatbots and to fool a human conversing them that they are responding intelligently. Of course they're gonna be useless at other tasks.

(I assume they're just using a big LLM for this, it doesnt say, it just says "AI" when they say "AI like that they usually mean LLM".. A custom trained hiring ML system would be better)

engineer_22•22m ago
Isn't HR basically just an LLM with ears and teeth?
bakugo•25m ago
> To put this in perspective: If the AI had recommended Black and Asian candidates at the same rate as it recommended the most-favored group (typically white applicants)

Some people just can't help but put their biases on display at every opportunity, even when it comes to the most minute details.

moate•6m ago
Where do you think this sentence shows bias?

The phrase "most-favored" means, "most recommended by the AI relative to the field".

What did you think this sentence meant?

x313•24m ago
This study only looks at one specific vendor algorithmn (a job assesment given by a company called pymetrics)
all2•18m ago
LLMs are trained on the Internet, which isn't exactly known for it's race agnostic opinions.
logicchains•22m ago
Could the AI actually see the race of the applicants? Or was it just discriminating on the basis of some factor it found that was correlated with race, like SAT scores?
dash2•20m ago
> To measure adverse impact, we apply the EEOC’s “four-fifths rule,” which flags a position when one group is recommended at less than 80% of the rate of the most-recommended group

That seems like a nonsensical way to measure racial discrimination. What could justify it?

nemomarx•16m ago
I guess it measures if there's more than one std deviation gap between highest and lowest? Assuming that's twenty percent here

it sounds like how you'd get that kind of metric at least

moate•14m ago
It's a starting point to flag.

Here's some analysis that took me 2 seconds of googling to find for you since you're clearly so curious: https://www.prevuehr.com/resources/insights/adverse-impact-a...

dash2•8m ago
Thanks. I read the article:

> Since the 80% test does not involve probability distributions to determine whether the disparity is a “beyond chance” occurrence, it is usually not regarded as a definitive test for adverse impact. Instead, other statistically significance tests, such as the standard deviation analysis, may be used for this purpose.

But then my question recurs: isn’t this a ridiculous way to measure discrimination? It’s assuming that the only thing that differs between the different ethnic applicant pools is their ethnicity, which is essentially never going to be true.

gacgacgac•
groundzeros2015•19m ago
I don’t think AI screening is effective. But this study is just disparate impact.
engineer_22•17m ago
> Using our large dataset of real hiring AI recommendations, we test our hypothesis. We find that people who submit multiple applications to positions screened by the same algorithmic hiring vendor are more likely to be rejected from every position to which they apply than would be true if the companies made decisions statistically independently from one another.

I would be surprised if the results were different.

verteu•16m ago
The paper is here: https://arxiv.org/pdf/2605.27371

They find "disparate impact" of pymetrics across racial groups, but it doesn't seem like they controlled for anything.

wand3r•15m ago
Did I miss the part of the article where they break down how they determined race? Is the algorithm blind to race? It looks like they specifically looked at 83k people applying to ~100 companies which notably were Fortune 500 companies. Could there simply be candidate discrepancies here? Hard for me to follow the full methodology but it doesn't necessarily seem either malicious or that well structured. Don't you need to have a control group of applicants who are similar on paper? To allege DISCRIMINATION is quite bold.

Definitely open to opposing or critical views

xrd•13m ago
Would be very interested to see how this affects post-50 workers. That's a protected class and I would imagine an ambulance chasing lawyer would be excited for a class action lawsuit.
black6•9m ago
I'm struggling to figure out what they're trying to say here in the linked (and very anemic) paper:

> 30% of Black applicants apply to at least one position that demonstrates adverse impact against Black applicants.

The whole thing reads like a tautology.

tamimio•6m ago
You don’t need a complicated study to find out, do it yourself for science. Get a resume, make few different versions but keep the context the same, change the layout (one time education on top other on bottom etc etc), and use different names to signal different backgrounds, and you can extend it to schools too and gender, and send it to the same employers, you will see wonders!!

I tried it before, and discrimination is there, I would get one resume rejected quickly and few days later the same company would invite another resume for a screening call. I tried this before and after AI hype, results weren’t that different btw, and that was tested in US and Canada employers only.

4m ago
It's not used to measure discrimination. It's used to identify outcomes that appear to be potentially discriminatory. You have to do the legwork afterwards.

Like. If I am evaluating a developer on lines of code written, I am a bad manager. But if an engineer has 40% fewer lines of code than the team median, it's absolutely ok for me to go, "Interesting. What's the story there? Are they slower or is there some other factor?"

Same idea -- this is purely a fast, first pass metric that can quickly assess if something warrants a deeper evaluation.

logicchains•13m ago
>What could justify it?

The assumption that applicants from all races are on average equally qualified for every position. Whole subfields of modern academia are based on that assumption.

gacgacgac•9m ago
Have you googled this? The EEOC is a federal agency, and they've published on this topic quite extensively. The four fifths rule is used to define if there is a "substantially different selection rate". It does not measure racial discrimination. It measures selection rate.

It indicates there may be adverse impact to one group. It specifically is not used to resolve racial discrimination.

It's purely a signal for "we should consider asking more questions, because this appears unusual". That's what your quote says too, it "flags" a low recommendation -- it's indicating further study and investigation is likely warranted.

paisawalla•9m ago
This is an application of the disparate impact doctrine. Even facially neutral policies are considered suspect if they produce results that correlate against protected groups, irrespective of intent.

This doctrine is the basis for much of employment law. It is a significant reason why employers don't administer IQ tests (or equivalents) to screen candidates since ~the 90s.

A common objection to the doctrine is that it leads to unfalsifiable discrimination claims, which is why it seems nonsensical to you.

gacgacgac•7m ago
Importantly, the rule is not used to resolve racial discrimination claims. It's purely meant as the first test to evaluate whether a deeper dive is warranted. Fast, first pass data analysis tools are very useful for spotting unintended consequences.

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