How can you combat bias in a team of humans if humans are susceptible to bias? How do you even know what neutral is if it's not a physically measurable quantity?

This sounds like an argument for never educating people and doing nothing about the problem.

Just because you graduated from a CS program doesn't mean you will always write bug free code using the ideal algorithm and design pattern.

But you know what you should be striving for, can more readily identify issues, and maybe sometimes you actually are perfect.

> This sounds like an argument for never educating people and doing nothing about the problem.

I feel like it's really the opposite.

There is a major problem with using AI to screen CVs, which is that AI is bad at screening CVs. It excludes good candidates and offers mediocre ones, and opens up a bunch of hacks the equivalent of black hat SEO, which gives an undesired advantage to people inclined to dirty tricks.

But "it's racist" is a political hot button, so before people can start to point that out, someone scrambles to push the hot button and suck all the oxygen out of the room. Then the purveyors of snake oil can munge the algorithm until it passes the naive oversimplified racial bias test someone made up (probably making it even worse at its intended purpose), and then claim "the problem" is solved, as if that was the main and only problem.

They send resumes with the same accomplishments and names that indicate race and observe the relative degrees of acceptance.

Everytime they do this test, considerable bias is found to still exist.

remove names then. problem solved.

There are still plenty of proxies that can amount to the same thing. Not a lot of white graduates of HBCUs, for instance. Are we removing the names of all schools, too?

if you dont want to favor ivy leagues that would be the next thing to remove yes

Still using resumes, have an ai interview for knowledge and communication skill.

Like many others, we've built a product to do this.

I think it gives a much higher signal on candidates than a resume, and is more "blind" in terms of bias than a human.

It's based on voice messages. Open to all here if anyone wants to take it for a spin. https://candit.net

How do you get an unbiased evaluation of someone with an accent that differs from your training set? It took about a decade for my friends with Indian English accents to be able to use voice controls in luxury cars. And honestly young people from India have a much more American accent than my generation.

It‘s 2024. There are decades or research with a lot of data, there exist best practices and products that help combating the bias. The toolkit available to HRs is rich and there’s plenty of things that work. E.g. I have absolutely no problem hiring women in tech roles, because I simply adjusted my process. I heard complaints from other engineering leaders that they want to increase diversity but struggle to find candidates: they do not realize that they have bias in job descriptions, they apply same screening criteria and do the post-interview evaluation the same way regardless of candidate gender or background. Many people can do the job, but they communicate it differently and it is important to account for that when you see the CV or talk to them.

Isn't not knowing that bias exists just the same problem that the average HR department has? You solve that by educating people about biases. If anything that should be easier with LLMs because you're not asking people to alter their sense of self ("I wouldn't do that!"), you're just informing them of technical limitations.

I don’t understand your argument. The whole purpose of AI-driven recruitment is to reduce human effort by doing some work for them. If AI due to existing bias reduces diversity in your pool of candidates, what can you do with it? You either do not notice the bias and just think that those people are indeed the best fit, you willingly accept the bias („But it reduces our hiring costs! We deal with diversity later, when this technology matures!“), or you go to see the unfiltered stream and put such an amount of work into it that makes AI useless. HR departments are not the people who will build their own solution, they will buy it from a third party and they won’t have control over or the budget for the fine-tuning.

of course you can know it exists. you just need to observe inputs and outputs and compare with precious non LLM ways.

You do not understand how HR works then. They are not LLM researchers and just apply the available toolkit, e.g. organizing regular training for hiring managers. When they buy AI-driven solution from a third party, they have no tools neither to detect if it’s just a statistical fluctuation or there’s a bias they need to find a workaround for, nor to act on this information in a cost-efficient way. Most likely they won‘t even have enough data to reach any statistical significance. The vendor in theory could do it, but most of them are startups now and they do not have diversity in their OKRs, at least not now. It’s not the UVP of their product.