Yes. In my case, my particular worldview involves (i) indifferent to gender markers in text (I don't want my LLM to convert me to a female because I'm asking it to write a cover letter for a hairdresser/secretary/nurse role, for e.g.). I don't want my model to write it in AAVE if my name is a common african-american name. I don't want to get the best results only when I write as a middle-aged white man [1]. If I use the model for some decision making, I want it to be fair for subgroups [2], which is a reasonably objective metric [3].

[1] https://aclanthology.org/P19-1339.pdf [2] https://arxiv.org/pdf/1906.09208.pdf [3] https://developers.google.com/machine-learning/glossary/fair...