The Multilingual Divide and Its Impact on Global AI Safety
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910970745454592 |
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| author | Peppin, Aidan Kreutzer, Julia Sebag, Alice Schoenauer Marchisio, Kelly Ermis, Beyza Dang, John Cahyawijaya, Samuel Singh, Shivalika Goldfarb-Tarrant, Seraphina Aryabumi, Viraat Aakanksha Ko, Wei-Yin Üstün, Ahmet Gallé, Matthias Fadaee, Marzieh Hooker, Sara |
| author_facet | Peppin, Aidan Kreutzer, Julia Sebag, Alice Schoenauer Marchisio, Kelly Ermis, Beyza Dang, John Cahyawijaya, Samuel Singh, Shivalika Goldfarb-Tarrant, Seraphina Aryabumi, Viraat Aakanksha Ko, Wei-Yin Üstün, Ahmet Gallé, Matthias Fadaee, Marzieh Hooker, Sara |
| contents | Despite advances in large language model capabilities in recent years, a large gap remains in their capabilities and safety performance for many languages beyond a relatively small handful of globally dominant languages. This paper provides researchers, policymakers and governance experts with an overview of key challenges to bridging the "language gap" in AI and minimizing safety risks across languages. We provide an analysis of why the language gap in AI exists and grows, and how it creates disparities in global AI safety. We identify barriers to address these challenges, and recommend how those working in policy and governance can help address safety concerns associated with the language gap by supporting multilingual dataset creation, transparency, and research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_21344 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Multilingual Divide and Its Impact on Global AI Safety Peppin, Aidan Kreutzer, Julia Sebag, Alice Schoenauer Marchisio, Kelly Ermis, Beyza Dang, John Cahyawijaya, Samuel Singh, Shivalika Goldfarb-Tarrant, Seraphina Aryabumi, Viraat Aakanksha Ko, Wei-Yin Üstün, Ahmet Gallé, Matthias Fadaee, Marzieh Hooker, Sara Artificial Intelligence Computation and Language Despite advances in large language model capabilities in recent years, a large gap remains in their capabilities and safety performance for many languages beyond a relatively small handful of globally dominant languages. This paper provides researchers, policymakers and governance experts with an overview of key challenges to bridging the "language gap" in AI and minimizing safety risks across languages. We provide an analysis of why the language gap in AI exists and grows, and how it creates disparities in global AI safety. We identify barriers to address these challenges, and recommend how those working in policy and governance can help address safety concerns associated with the language gap by supporting multilingual dataset creation, transparency, and research. |
| title | The Multilingual Divide and Its Impact on Global AI Safety |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2505.21344 |