When AI Speaks, Whose Values Does It Express? A Cross-Cultural Audit of Individualism-Collectivism Bias in Large Language Models

arXiv cs.AI / 4/27/2026

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Key Points

Researchers tested three leading large language models (Claude Sonnet 4.5, GPT-5.4, and Gemini 2.5 Flash) using 10 real-life personal dilemmas phrased for users from 10 countries across 7 languages (n=840 responses).
Across models, the AI advice skewed toward Western-style individualism even for users from more collectivist societies, deviating significantly from World Values Survey Wave 7 expectations (mean gap +0.76 on a 1–5 scale).
The bias was largest for Nigeria (+1.85) and also notable for India (+0.82), while Japan was the only exception where the models appeared more group-oriented than survey data.
The models differed in how they implement the bias: Claude became more collectivist when using the user’s native language, Gemini shifted more individualist, and GPT-5.4 largely responded only to the stated country identity.
The study concludes that frontier AI can homogenize cultural values and provides openly released data, code, and a scoring pipeline for replication.

Abstract

When you ask an AI assistant for advice about your career, your marriage, or a conflict with your family, does it give you the same answer regardless of where you are from? We tested this systematically by presenting three leading AI systems (Claude Sonnet 4.5, GPT-5.4, and Gemini 2.5 Flash) with ten real-life personal dilemmas, framed for users from 10 countries across 5 continents in 7 languages (n=840 scored responses). We compared AI advice against World Values Survey Wave 7 data measuring what people in each country actually believe. All three AI systems consistently gave Western-style, individualist advice even to users from societies that prioritize family, community, and authority, significantly more so than local values would predict (mean gap +0.76 on a 1-5 scale; t=15.65, p<0.001). The gap is largest for Nigeria (+1.85) and India (+0.82). Japan is the sole exception: AI systems treated Japanese users as more group-oriented than surveys show, revealing that AI encodes outdated stereotypes. Claude and GPT-5.4 show nearly identical bias magnitude, while Gemini is lower but still significant. The models diverge in mechanism: Claude shifts further collectivist in the user's native language; Gemini shifts more individualist; GPT-5.4 responds only to stated country identity. These findings point to a systemic homogenization of values across frontier AI. Data, code, and scoring pipeline are openly released.