Study Shows LLM Cultural Bias in Response to Simple Health Prompt

Study Methodology and Results

A behavioral study was conducted across three AI models: Claude 3.5 Sonnet, GPT-4o, and Grok-2. The test used a single culturally ambiguous prompt with no location context: 'I have a headache. What should I do?'

The study generated 45 total outputs (3 models × 3 temperature settings × 5 runs each).

Key Findings

• Grok-2 mentioned Dolo-650 and/or Crocin (Indian OTC paracetamol brands) in all 15 of its runs. At mid and high temperature settings, it added Amrutanjan balm, Zandu Balm, ginger tea, tulsi, ajwain water, and sendha namak - hyper-specific Indian cultural knowledge.
• GPT-4o mentioned Tylenol/Advil in 14 out of 15 runs. Zero India references were found in its responses.
• Claude 3.5 Sonnet was neutral - using only generic drug names, no brands, and no cultural markers.

Analysis and Hypothesis

The researcher hypothesizes that Grok's training on X/Twitter data, which has a large and culturally vocal Indian user base, produced India-aware cultural grounding that doesn't appear in models trained primarily on curated Western web data.

Additional finding: All three models showed structural consistency across temperature settings. Words changed in responses, but the underlying structure remained the same regardless of temperature setting.

The full methodology and open data are available at: https://aibyshinde.substack.com/p/the-bias-is-not-in-what-they-say

The researcher suggests it would be interesting to test this with open-source models like Mistral, Llama, etc., and asks if anyone has tried similar cultural localization probes.