> That's just your claim about how LLMs "should" work, based on ... your subjective preference?
Nothing subjective at all. Given 2 unknown races with no data on either, the result of hiring should be equally split between them. If you don't observe an equal split, there is a hidden bias.
Why do you think that is subjective? If you roll a die 100 times and observe that 6 comes up about 50% of the time, would you still call someone subjective when they say "that should not happen"?
It's a bias even if the true population distribution isn't linear.
For example, if you have a training corpus where 50% of the text follows "black bobblehead" with "arrested" and 20% of the text follows "white bobblehead" with "arrested", and your LLM is trained such that it produces "arrest" 50% of time after "<color> bobblehead" regardless of color, that's a bias - the output frequency distribution fails to match the "population" (training) frequency distribution. This has nothing to do with races, ethnicities, whatever - it's just statistics and text. To be unbiased, it would need to be less likely to produce the text "arrested" after "white bobblehead" than after "black bobblehead".
A die is supposed to land on each face evenly - a linear probability distribution. So anything other than a linear distribution is biased. But bias can exist for any desired probability distribution. And for an LLM the desired probability distribution of the model output is one that exactly matches the infinitely-many distributions of the various facets of the training data.
Your point about how in the absense of information a token shouldn't influence the distribution is spot-on. But unfortunately almost any token does condition the output, which means you get biased output all the time.