A "Perspectival" Mirror of the Elephant: Investigating Language Bias on Google, ChatGPT, YouTube, and Wikipedia
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Abstract (arXiv)
Contrary to Google Search's mission of delivering information from "many angles so you can form your own understanding of the world," we find that Google and its most prominent returned results - Wikipedia and YouTube - simply reflect a narrow set of culturally dominant views tied to the search language for complex topics like "Buddhism," "Liberalism," "colonization," "Iran" and "America." Simply stated, they present, to varying degrees, distinct information across the same search in different languages, a phenomenon we call language bias. This paper presents evidence and analysis of language bias and discusses its larger social implications. We find that our online searches and emerging tools like ChatGPT turn us into the proverbial blind person touching a small portion of an elephant, ignorant of the existence of other cultural perspectives. Language bias sets a strong yet invisible cultural barrier online, where each language group thinks they can see other groups through searches, but in fact, what they see is their own reflection.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 1 of its 65 references that are in the map. First posted on arXiv 28 Mar 2023.
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1 of 65 references.
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Citing papers per year: 2023: 1; 2024: 4; 2025: 3.
8 papers.
- 2024Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systemscited by 66
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- 2024Stars, Stripes, and Silicon: Unravelling the ChatGPT's All-American, Monochrome, Cis-centric Biascited by 4
- 2024The Social Impact of Generative LLM-Based AIcited by 3
- 2023Dialogue Agents 101: A Beginner's Guide to Critical Ingredients for Designing Effective Conversational Systemscited by 1
- 2025A Peek Behind the Curtain: Using Step-Around Prompt Engineering to Identify Bias and Misinformation in GenAI Modelscited by 0
- 2025An Empirical Investigation of Gender Stereotype Representation in Large Language Models: The Italian Casecited by 0
- 2025Can Large Language Models (LLMs) Describe Pictures Like Children? A Comparative Corpus Studycited by 0
- 2025Can Large Language Models (LLMs) Describe Pictures Like Children? A Comparative Corpus Study19 Aug 2025
- 2025An Empirical Investigation of Gender Stereotype Representation in Large Language Models: The Italian Case25 Jul 2025
- 2025A Peek Behind the Curtain: Using Step-Around Prompt Engineering to Identify Bias and Misinformation in GenAI Models19 Mar 2025
- 2024The Social Impact of Generative LLM-Based AI11 Oct 2024
- 2024Stars, Stripes, and Silicon: Unravelling the ChatGPT's All-American, Monochrome, Cis-centric Bias2 Oct 2024
- 2024Fairness in Large Language Models: A Taxonomic Survey31 Mar 2024
- 2024Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems11 Jan 2024
- 2023Dialogue Agents 101: A Beginner's Guide to Critical Ingredients for Designing Effective Conversational Systems14 Jul 2023