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.
:)
Screened Applications [13]
Unscreened Applications [39148](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)
Some people just can't help but put their biases on display at every opportunity, even when it comes to the most minute details.
The phrase "most-favored" means, "most recommended by the AI relative to the field".
What did you think this sentence meant?
That seems like a nonsensical way to measure racial discrimination. What could justify it?
it sounds like how you'd get that kind of metric at least
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...
> 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.
I would be surprised if the results were different.
They find "disparate impact" of pymetrics across racial groups, but it doesn't seem like they controlled for anything.
Definitely open to opposing or critical views
> 30% of Black applicants apply to at least one position that demonstrates adverse impact against Black applicants.
The whole thing reads like a tautology.
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.
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.
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.
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.
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.
anonfunction•53m ago
Happy to share some sample reports if anyone is interested!
1. https://www.latentevals.com/
etchalon•43m ago