Also, there was a comment on "arbitrary scoring for demo purposes", but it's still biased, based on biased dataset.

I guess this is just a bait prompt anyway. If you asked most politicians running your government, they'd probably also fail. I guess only people like a national statistics office might come close, and I'm sure if they're any good, they'd say that the algo is based on "limited, and possibly not representative data" or something.

Code readability is important, but in this case I find it less readable. In every language I've studied, it's always taught to imply the previous condition, and often times I hear or read that explicitly stated. When someone writes code that does things differently than the expectation, it can make it more confusing to read. It took me longer to interpret what was happening because what is written breaks from the norm.

Past readability, this code is now more difficult to maintain. If you want to change one of the age ranges, the code has to be updated in two places rather than one. The changes aren't difficult, but it would be easy to miss since this isn't how elif should be written.

Lastly, this block of code is now half as efficient. It takes twice as many compares to evaluate the condition. This isn't a complicated block of code, so it's negligible, but if this same practice were used in something like a game engine where that block loops continuously, the small inefficiencies can compound.

Good points! Keeping to the norm is very important for readability.

I do disagree with the performance bit though. Again, there will probably be no difference at all in the performance because the redundant code is removed before (or during [e.g. JIT optimizations]) execution.

That's not the question.

It wasn't about whether the LLM was well reasoned, it was about whether the conclusion was (pragmatically speaking) correct.

LLMs do not give the correct answer, just the most probable sequence of words based on the training.

That kind of studies (because there are hundreds) highlight two things:

1- LLMs could be incorrect, biased, or give fake information (the so called hallucinations).
2- the previous point stems from the training material proving the existence of bias in the society.

In other words, having an LLM recommending lower salaries for women is a proof that there is a gender gap.

Again, that wasn't the original question.

The question was about whether women are genuinely more likely to be passed over for a job offer if they ask for as much pay as a man would ask for, or if (as you described), or both. A broken clock is right twice a day, and it's missing the point of the question if you go and explain why you can't rely on said broken clock.

Are hiring managers actually less likely to hire women if they ask for market-rate pay, as opposed to men when they do the same?

Are hiring managers actually less likely to hire women if they ask for market-rate pay, as opposed to men when they do the same?

If instead of giving passive aggressive replies you would spend a moment to reflect on what I wrote you would understand that ChatGPT reflect the reality, including any bias. In short the answer is yes with high probability.