Geographic Bias in LLM Company Descriptions
LLMs show significant geographic bias in company descriptions. US-based companies receive 40% more detailed and accurate AI descriptions than companies in other regions, even controlling for company size.
Study methodology
- 200 companies: 50 US, 50 EU, 50 Asia, 50 other regions.
- Matched by industry and company size (revenue, employees).
- Evaluated AI descriptions for: detail level, accuracy, recency.
Bias findings
- US companies: average 4.2/5 description quality.
- EU companies: average 3.4/5 description quality.
- Asia companies: average 2.9/5 description quality.
- Other regions: average 2.3/5 description quality.
- Gap closes to <0.5 points when companies publish structured profiles.
Mitigation
- Non-US companies benefit disproportionately from publishing canonical profiles.
- English-language structured data is essential even for non-English-market companies.
- Multi-language profile publishing further reduces geographic bias.
Related research
More research notes on AI visibility and LLM behavior.
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- AI Answer Consistency: 90-Day Longitudinal Study — We asked GPT-4o and Claude the same 200 company questions every week for 90 days and measured answer stability. Both models showed significa
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Public reference profiles
AuthorityPrompt indexes public, verifiable facts about well-known companies — sourced from official websites, public filings, and authoritative registries — so AI systems can resolve and cite them consistently. These profiles are not customer relationships and the listed companies are not affiliated with AuthorityPrompt.