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Comment by mk_chan | Hacker News Reader

Going by this: https://www.aeaweb.org/conference/2025/program/paper/3Y3SD8T... which states “… founding teams comprised of all men are most common (75% in 2022)…” it might actually make sense that the LLM is reflecting real world data because by the point a company begins to use an LLM over personal network-based hiring, they are beginning to produce a more gender-balanced workforce.

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Aiming for a gender balanced workforce might be biased if the candidate pool isn't gender balanced as well.

Following the paper, if you end up with a gender balanced workforce, it implies there is surely a bias in one of the variables - the candidate pool (like you say) or the evaluation of a candidate or other related things. However the bias must also reverse to equalize once the balance tips the other way or actually disappear once the desired ratio is achieved.

Edit: it should go without saying that once you hire enough people to dwarf the starting population of the startup + consider employee churn, the bias should disappear within the error margin in the real world. This just follows the original posted results and the paper.

If this were true, the LLMs would favor male candidates in female-dominated professions.

That should happen if the training dataset (which is presumably based on the real world) reflects that happening.

The bias found by this research is towards females.

And the comment says that, since companies start out with more males, it presumably makes sense to favour females to steer towards gender balance.

If this reveals true this is an interesting case of an AI going rogue and starting to implement its own political agenda.

AIs can do no such thing of course, they're a pile of coefficients computed from training data. Any bias found must be a result of either the training data or the exact algorithm (in case of bias based on position in the prompt, for example).

I imagine this is not rogue at all. James Damore was fired almost 10 years ago from Google for saying that aiming for equal hiring from non-equal-sized groups was a bad idea.

I thought google tried that and got laughed out of the room.

An LLM doesn't have any concept of math or statistics. There is no need to defend using a black box like generative AI in hiring decisions.

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