AI tools are making the world look weird (strat7.com)

A recent analysis highlighted by STRAT7 and based on a 2023 Harvard study shows that large language models like ChatGPT encode distinctly “WEIRD” (Western, Educated, Industrialised, Rich, Democratic) cultural assumptions. The researchers administered the World Values Survey (WVS) to ChatGPT 1,000 times and compared responses to real-country WVS data, finding strong alignment with Western countries (and surprisingly close fit to smaller Western nations like New Zealand) but rapidly decreasing accuracy as cultural distance from the U.S. grew—responses for places such as Libya and Pakistan were little better than chance. The work isolates not just generic bias but a specific cultural skew: LLM outputs tend to reflect American/Californian norms because of training data and industry geography. This matters for AI/ML practitioners, researchers and product teams because off‑the‑shelf models can flatten or misrepresent diverse values during analysis, moderation and design—creating a “double jeopardy” where under-resourced non-WEIRD contexts get both less research investment and poorer model fit. Technical implications include biased priors in downstream analytics, unreliable cross-cultural inference, and loss of local nuance. Practical mitigations: measure model-country correlation, diversify model sources, fine-tune with local data, embed local expertise in pipelines, and evaluate cultural “fitness” continuously. STRAT7 plans cross-continental LLM experiments; practitioners should similarly test models against local ground truth before deploying them internationally.