Diversity and language technology: how language modeling bias causes epistemic injustice
It is well known that AI-based language technology—large language models, machine translation systems, multilingual dictionaries, and corpora—is currently limited to three percent of the world’s most widely spoken, financially and politically backed languages. In response, recent efforts have sought...
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| Vydáno v: | Ethics and information technology Ročník 26; číslo 1; s. 8 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Dordrecht
Springer Netherlands
01.03.2024
Springer Nature B.V Springer Verlag |
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| ISSN: | 1388-1957, 1572-8439 |
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| Abstract | It is well known that AI-based language technology—large language models, machine translation systems, multilingual dictionaries, and corpora—is currently limited to three percent of the world’s most widely spoken, financially and politically backed languages. In response, recent efforts have sought to address the “digital language divide” by extending the reach of large language models to “underserved languages.” We show how some of these efforts tend to produce flawed solutions that adhere to a hard-wired representational preference for certain languages, which we call language modeling bias. Language modeling bias is a specific and under-studied form of linguistic bias were language technology by design favors certain languages, dialects, or sociolects with respect to others. We show that language modeling bias can result in systems that, while being precise regarding languages and cultures of dominant powers, are limited in the expression of socio-culturally relevant notions of other communities. We further argue that at the root of this problem lies a systematic tendency of technology developer communities to apply a simplistic understanding of diversity which does not do justice to the more profound differences that languages, and ultimately the communities that speak them, embody. Drawing on the concept of epistemic injustice, we point to the broader ethico-political implications and show how it can lead not only to a disregard for valuable aspects of diversity but also to an under-representation of the needs of marginalized language communities. Finally, we present an alternative socio-technical approach that is designed to tackle some of the analyzed problems. |
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| AbstractList | It is well known that AI-based language technology—large language models, machine translation systems, multilingual dictionaries, and corpora—is currently limited to three percent of the world’s most widely spoken, financially and politically backed languages. In response, recent efforts have sought to address the “digital language divide” by extending the reach of large language models to “underserved languages.” We show how some of these efforts tend to produce flawed solutions that adhere to a hard-wired representational preference for certain languages, which we call language modeling bias. Language modeling bias is a specific and under-studied form of linguistic bias were language technology by design favors certain languages, dialects, or sociolects with respect to others. We show that language modeling bias can result in systems that, while being precise regarding languages and cultures of dominant powers, are limited in the expression of socio-culturally relevant notions of other communities. We further argue that at the root of this problem lies a systematic tendency of technology developer communities to apply a simplistic understanding of diversity which does not do justice to the more profound differences that languages, and ultimately the communities that speak them, embody. Drawing on the concept of epistemic injustice, we point to the broader ethico-political implications and show how it can lead not only to a disregard for valuable aspects of diversity but also to an under-representation of the needs of marginalized language communities. Finally, we present an alternative socio-technical approach that is designed to tackle some of the analyzed problems. |
| ArticleNumber | 8 |
| Author | Bella, Gábor Giunchiglia, Fausto Helm, Paula Koch, Gertraud |
| Author_xml | – sequence: 1 givenname: Paula orcidid: 0000-0002-2719-9721 surname: Helm fullname: Helm, Paula email: p.m.helm@uva.nl organization: University of Amsterdam – sequence: 2 givenname: Gábor surname: Bella fullname: Bella, Gábor organization: Lab-STICC CNRS UMR 628, IMT Atlantique – sequence: 3 givenname: Gertraud surname: Koch fullname: Koch, Gertraud organization: University of Hamburg – sequence: 4 givenname: Fausto surname: Giunchiglia fullname: Giunchiglia, Fausto organization: University of Trento |
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| Keywords | Language technology Large language models Epistemic injustice Diversity Language modeling bias Linguistic bias Digital divide Lexical gaps epistemic injustice language modeling bias bias linguistic diversity |
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| SubjectTerms | Artificial Intelligence Bias Bilingual dictionaries Community Computation and Language Computer Science Computers and Society Dialects Dictionaries Ethics History, Philosophy and Sociology of Sciences Humanities and Social Sciences Innovation/Technology Management Justice Language Language modeling Languages Large language models Library Science Machine translation Management of Computing and Information Systems Marginality Modelling Original Paper Sociolects Speech communities Technology Underserved populations User Interfaces and Human Computer Interaction |
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| Title | Diversity and language technology: how language modeling bias causes epistemic injustice |
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