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Abstract: 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 the narrow set of cultural stereotypes 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 (we call it 'language bias'). Instead of presenting a global picture of a complex topic, our online searches turn us into the proverbial blind person touching a small portion of an elephant, ignorant of the existence of other cultural perspectives. The language we use to search ends up as a cultural filter to promote ethnocentric views, where a person evaluates other people or ideas based on their own culture. We also find that language bias is deeply embedded in ChatGPT. As it is primarily trained on English language data, it presents the Anglo-American perspective as the normative view, reducing the complexity of a multifaceted issue to the single Anglo-American standard. In this paper, we present evidence and analysis of language bias and discuss its larger social implications. Toward the end of the paper, we propose a potential framework of using automatic translation to leverage language bias and argue that the task of piecing together a genuine depiction of the elephant is a challenging and important endeavor that deserves a new area of research in NLP and requires collaboration with scholars from the humanities to create ethically sound and socially responsible technology together.

Submission history

From: Queenie Luo [view email]
[v1] Tue, 28 Mar 2023 19:49:58 UTC (3,288 KB)

||||I|||| Skip to main content We gratefully acknowledge support from the Simons Foundation and member institutions. > cs > arXiv:2303.16281 Help | Advanced Search All fields Title Author Abstract Comments Journal reference ACM classification MSC classification Report number arXiv identifier DOI ORCID arXiv author ID Help pages Full text Search GO quick links * Login * Help Pages * About Computer Science > Computers and Society arXiv:2303.16281 (cs) [Submitted on 28 Mar 2023] Title: A Perspectival Mirror of the Elephant: Investigating Language Bias on Google, ChatGPT, Wikipedia, and YouTube Authors: Queenie Luo, Michael J. Puett, Michael D. Smith Download a PDF of the paper titled A Perspectival Mirror of the Elephant: Investigating Language Bias on Google, ChatGPT, Wikipedia, and YouTube, by Queenie Luo and 2 other authors Download PDF Abstract: 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 the narrow set of cultural stereotypes 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 (we call it 'language bias'). Instead of presenting a global picture of a complex topic, our online searches turn us into the proverbial blind person touching a small portion of an elephant, ignorant of the existence of other cultural perspectives. The language we use to search ends up as a cultural filter to promote ethnocentric views, where a person evaluates other people or ideas based on their own culture. We also find that language bias is deeply embedded in ChatGPT. As it is primarily trained on English language data, it presents the Anglo-American perspective as the normative view, reducing the complexity of a multifaceted issue to the single Anglo-American standard. In this paper, we present evidence and analysis of language bias and discuss its larger social implications. Toward the end of the paper, we propose a potential framework of using automatic translation to leverage language bias and argue that the task of piecing together a genuine depiction of the elephant is a challenging and important endeavor that deserves a new area of research in NLP and requires collaboration with scholars from the humanities to create ethically sound and socially responsible technology together. Subjects: Computers and Society (cs.CY) ; Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Social and Information Networks (cs.SI) Cite as: arXiv:2303.16281 [cs.CY] (or arXiv:2303.16281v1 [cs.CY] for this version) https://doi.org/10.48550/arXiv.2303.16281 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Queenie Luo [view email] [v1] Tue, 28 Mar 2023 19:49:58 UTC (3,288 KB) Full-text links: Download: Download a PDF of the paper titled A Perspectival Mirror of the Elephant: Investigating Language Bias on Google, ChatGPT, Wikipedia, and YouTube, by Queenie Luo and 2 other authors * PDF only (license) Current browse context: cs.CY < prev | next > new | recent | 2303 Change to browse by: cs cs.AI cs.CL cs.LG cs.SI References & Citations * NASA ADS * Google Scholar * Semantic Scholar a export BibTeX citation Loading... BibTeX formatted citation × loading... 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