# A "Perspectival" Mirror of the Elephant: Investigating Language Bias on Google, ChatGPT, YouTube, and Wikipedia

arXiv: https://arxiv.org/abs/2303.16281 · HTML: https://mlsanity.com/paper/2303.16281/

## 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.

## Builds on (most cited first, 1 of 1)

- [Speaking Multiple Languages Affects the Moral Bias of Language Models](https://mlsanity.com/paper/2211.07733/) (2022)

## Built on by (most cited first, 8 of 8)

- [Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems](https://mlsanity.com/paper/2401.05778/) (2024)
- [Fairness in Large Language Models: A Taxonomic Survey](https://mlsanity.com/paper/2404.01349/) (2024)
- [Stars, Stripes, and Silicon: Unravelling the ChatGPT's All-American, Monochrome, Cis-centric Bias](https://mlsanity.com/paper/2410.13868/) (2024)
- [The Social Impact of Generative LLM-Based AI](https://mlsanity.com/paper/2410.21281/) (2024)
- [Dialogue Agents 101: A Beginner's Guide to Critical Ingredients for Designing Effective Conversational Systems](https://mlsanity.com/paper/2307.07255/) (2023)
- [A Peek Behind the Curtain: Using Step-Around Prompt Engineering to Identify Bias and Misinformation in GenAI Models](https://mlsanity.com/paper/2503.15205/) (2025)
- [An Empirical Investigation of Gender Stereotype Representation in Large Language Models: The Italian Case](https://mlsanity.com/paper/2507.19156/) (2025)
- [Can Large Language Models (LLMs) Describe Pictures Like Children? A Comparative Corpus Study](https://mlsanity.com/paper/2508.13769/) (2025)

## 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.