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.

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369548684892826 4 hours ago | parent [-]

Great 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

	▲	TeMPOraL an hour ago \| parent \| next [-]
		LLMs 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.
	▲	cyanydeez an hour ago \| parent \| prev [-]
		this 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 nonetheless, people will defend history as perfect and say those samples, like nepo babies, are "perfect".