I would expect that to depend on jurisdiction.
replyI don't know for sure, but I would be surprised if it was illegal in my particular US state. You might be able to argue the AI has inherent biases that introduce illegal discrimination in the hiring process, but my understanding is winning I case like that would be very difficult, especially since most employers are very cagey about their hiring process and why they mades a decision.
They don't need to actually filter/blackhole to have have the same virtual effect.
replyShow someone a list of resumes with an "applicant score*" and they'll naturally ignore the ones with a low ranking
*scores are generated with AI, mistakes may be made, use only as a guide and verify results
In situations when you get hundreds of applications for one open position (real market now), whatever reduces your pool to the size a human can handle, works. You can preserve some diversity metrics in the process. This particular filtering is rather primitive, but LLM as a first filter can definitely do the job. You may burn less tokens than the hourly rate of your HR and it will be fairer than just dumping 50% of unread CVs in trash.
replyGreat until someone realises you’ve filtered out minority groups from the application process (most developers are men so maybe the LLM decided they’re the best fit, but you’ll never know exactly why it screwed your over) and you suddenly have an expensive lawsuit
replyLLMs are DEI-aware, as over past few years, their vendors all had various high profile news stories with their models and their default biases, so it's more likely they'll heavily discriminate in favor of minority candidates, not against them. Still, in both cases it would indicate whoever is operating the system is doing a really, really lazy job. It's really not hard to test and supervise LLMs on tasks where they give you mere 2-10x leverage, and prompt adherence today is much better than it was 3 years ago.
replyJust last month: https://hai.stanford.edu/news/ai-hiring-tools-can-yield-raci...
replyWhat „not so smart“ person would filter minority groups out of the process in 2026? It‘s more likely that 90/10 gender disbalance will be converted to 60/40 or even 50/50. Diverse teams are more fun and stable.
replythis happened a decade ago when a US courted tried to make sentencing decisions via ML. it was easialy demonstrated that the training data was flawed because the justice system was flawed so the data it was trained on was weighted against minorities because it oversampled because you know, police routinely oversample and poverty for es oversampling
replynonetheless, people will defend history as perfect and say those samples, like nepo babies, are "perfect".
Under GDPR, you have the right to request manual processing whenever personal data is processed automatically to make a decision about you that has "significant impact". Not being hired seems like it would qualify.
replyIllegal where?
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