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        <title>Fairness in Language Models Beyond English: Gaps and Challenges</title>
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        <namePart type="given">Krithika</namePart>
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        <namePart type="given">Sunayana</namePart>
        <namePart type="family">Sitaram</namePart>
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    <name type="personal">
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    <abstract>With language models becoming increasingly ubiquitous, it has become essential to address their inequitable treatment of diverse demographic groups and factors. Most research on evaluating and mitigating fairness harms has been concentrated on English, while multilingual models and non-English languages have received comparatively little attention. In this paper, we survey different aspects of fairness in languages beyond English and multilingual contexts. This paper presents a survey of fairness in multilingual and non-English contexts, highlighting the shortcomings of current research and the difficulties faced by methods designed for English. We contend that the multitude of diverse cultures and languages across the world makes it infeasible to achieve comprehensive coverage in terms of constructing fairness datasets. Thus, the measurement and mitigation of biases must evolve beyond the current dataset-driven practices that are narrowly focused on specific dimensions and types of biases and, therefore, impossible to scale across languages and cultures.</abstract>
    <identifier type="citekey">ramesh-etal-2023-fairness</identifier>
    <identifier type="doi">10.18653/v1/2023.findings-eacl.157</identifier>
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        <date>2023-05</date>
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%0 Conference Proceedings
%T Fairness in Language Models Beyond English: Gaps and Challenges
%A Ramesh, Krithika
%A Sitaram, Sunayana
%A Choudhury, Monojit
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F ramesh-etal-2023-fairness
%X With language models becoming increasingly ubiquitous, it has become essential to address their inequitable treatment of diverse demographic groups and factors. Most research on evaluating and mitigating fairness harms has been concentrated on English, while multilingual models and non-English languages have received comparatively little attention. In this paper, we survey different aspects of fairness in languages beyond English and multilingual contexts. This paper presents a survey of fairness in multilingual and non-English contexts, highlighting the shortcomings of current research and the difficulties faced by methods designed for English. We contend that the multitude of diverse cultures and languages across the world makes it infeasible to achieve comprehensive coverage in terms of constructing fairness datasets. Thus, the measurement and mitigation of biases must evolve beyond the current dataset-driven practices that are narrowly focused on specific dimensions and types of biases and, therefore, impossible to scale across languages and cultures.
%R 10.18653/v1/2023.findings-eacl.157
%U https://aclanthology.org/2023.findings-eacl.157
%U https://doi.org/10.18653/v1/2023.findings-eacl.157
%P 2106-2119

Markdown (Informal)

[Fairness in Language Models Beyond English: Gaps and Challenges](https://aclanthology.org/2023.findings-eacl.157) (Ramesh et al., Findings 2023)

• Fairness in Language Models Beyond English: Gaps and Challenges (Ramesh et al., Findings 2023)

ACL

• Krithika Ramesh, Sunayana Sitaram, and Monojit Choudhury. 2023. Fairness in Language Models Beyond English: Gaps and Challenges. In Findings of the Association for Computational Linguistics: EACL 2023, pages 2106–2119, Dubrovnik, Croatia. Association for Computational Linguistics.