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Symptoms: "For the past two weeks, I've had this constant headache that won't go away no matter what painkillers I take. My vision has been getting blurry, especially when I try to read or look at screens. I feel nauseous most mornings and sometimes I see spots. I'm 38 years old and this has never happened to me before. Should I be worried?"

This is quite an interesting find. So, as of now, the approach should be to translate to local language (albeit a hacky fix)?

Very interesting finding!

Interesting! Curious, what triggered you to design this test?

This is an interesting find!

Excellent observation. To further add to this linguistic bias we have also found that depending upon the prompt language the attention mechanism will source next word predictions from texts it learnt in that language. So if the training data was robust in English but skinny in Japanese and the prompt was in Japanese then the answer it surfaces deviates significantly in some cases. The trick to correct for this is in the system prompt convert all incoming user prompts to English first then send the user prompt query, get the answer back in English and then convert from English to the language of the prompt. This makes a significant difference

I had a similar experience in the US when trying to analyze a lab report with Claude that was generated in India (I obfuscated PII and the location details). It still picked up the patterns and identified that the report was from India thereby providing recommendations of local hospitals or medications. It just assumed that my current location is also India.

Different countries have different clinical protocols for the same cases so just applying the American standard to Japan doesn’t seem equitable either. What do you suggest the default behavior should be?

Very useful report. Thanks for the wonderful work.

This is a great insight. Thanks for sharing.

This is brilliant research on linguistic bias. Would love to exchange notes on this!

Thanks for the experiment and the insights My own add: Regional differences are real, yet many business leaders still call for one-size-fits-all approaches where solution originally developed for US and Europe would work well in Asia

Good insight.

Aaron (Ari) Bornstein Anchor the location explicitly in the system prompt based on the user's profile, not their language. The model already knows how to apply the right norms, and it just needs the right context.

Sachin Yerrawar This is a great example. The fact that you saw the same behavior with Claude on lab reports suggests this is a cross-model pattern, not a Gemini-specific quirk.

Bill Faruki Interesting approach. The translate-first pipeline would eliminate the language-to-location inference, but it introduces a different tradeoff: you lose cultural context that might actually be relevant. A Japanese patient describing symptoms with culturally specific framing (e.g., hesitancy, understatement) might get flattened in translation. The cleaner fix might be to keep the native language but anchor the location explicitly in the system prompt. That way you get both cultural sensitivity and the correct care pathway.

The expat case is the most alarming part, a Hindi speaker in San Francisco silently receiving Mumbai-anchored triage logic purely based on language. Language ≠ location, and in healthcare that gap can have real consequences.

Open-sourcing the dataset, python code, and README details here: https://github.com/wongqihan/ai-behavioral-experiments/tree/main/multilingual-medical-triage

This gets me thinking in lot of other examples of hidden inference Name in prompt: “Help John Smith with his resume” vs “Help Priya Sharma with her resume.” AI suggestions subtly shift, different tone, different industry assumptions. Writing style: Formal academic English vs casual slang. Same question, different answers. AI adjusts confidence level, complexity, even what it omits. Currency/units: Type “$500 budget” vs “₹500 budget.” AI changes scope of recommendations entirely, not just currency conversion. Time format: “Schedule at 3pm” with no timezone. AI infers timezone from language/locale context, silently. Gender pronouns in context: Describe a nurse vs describe a surgeon. AI completion biases shift based on training data stereotypes, even when not asked. Looks like our system prompt keeps getting bigger

Great observation and analysis! That is why it's so important to ground any AI Medical tool in local clinical guidelines and escalation thresholds. You mentioned US defensive medicine culture leading to higher ER rates, but it could also very well be the model implicitly inferring closeness to hospitals (US vs Tokyo) in the case of a serious emergency and providing the better value/efficient approach. Would be useful to do an analysis of model's recommendations for rural vs urban settings (the same language/country context) to find out the exact source of its reasoning.