As someone from outside the US, it is quite common to face annoyances like address fields expecting addresses in US format, systems misbehaving and sometimes failing silently if you have two surnames, or accented characters in your personal data, etc. Years go by, tech gets better, but these issues don't go away, they just reappear in different places.

It's funny how some people seem to have discovered this kind of bias and started getting angry with LLMs, which are actually quite OK in this respect.

Not saying that it isn't an issue that should be addressed, just that some people are using it as an excuse to get indignant at AI and it doesn't make much sense. Just like the people who get indignant at AI because ChatGPT collects your input and uses it for training - what do they think social networks have been doing with their input in the last 20 years?

If there was no Germany-specific data in the training corpus - it is not fair to expect LLM to know anything about Germany.

You can check a foundation model from Chinese LLM researchers, and you will most likely see Sino-centric bias just because of the training corpus + synthetic data generation was focused on their native/working language, and their goal was to create foundation model for their language.

I challenge any LLM sceptics - instead of just lazily poking holes in models - create a supposedly better model that reduces bias and lets evaluate your model with specific metrics