ChatGPT Perpetuates Gender Bias in Machine Translation and Ignores Non-Gendered Pronouns: Findings across Bengali and Five other Low-Resource Languages

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Abstract

In this multicultural age, language translation is one of the most performed tasks, and it is becoming increasingly AI-moderated and automated. As a novel AI system, ChatGPT claims to be proficient in machine translation tasks and in this paper, we put that claim to the test. Specifically, we examine ChatGPT's accuracy in translating between English and languages that exclusively use gender-neutral pronouns. We center this study around Bengali, the 7th most spoken language globally, but also generalize our findings across five other languages: Farsi, Malay, Tagalog, Thai, and Turkish. We find that ChatGPT perpetuates gender defaults and stereotypes assigned to certain occupations (e.g., man = doctor, woman = nurse) or actions (e.g., woman = cook, man = go to work), as it converts gender-neutral pronouns in languages to 'he' or 'she'. We also observe ChatGPT completely failing to translate the English gender-neutral singular pronoun 'they' into equivalent gender-neutral pronouns in other languages, as it produces translations that are incoherent and incorrect. While it does respect and provide appropriately gender-marked versions of Bengali words when prompted with gender information in English, ChatGPT appears to confer a higher respect to men than to women in the same occupation. We conclude that ChatGPT exhibits the same gender biases which have been demonstrated for tools like Google Translate or MS Translator, as we provide recommendations for a human centered approach for future designers of AI systems that perform machine translation to better accommodate such low-resource languages.

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APA

Ghosh, S., & Caliskan, A. (2023). ChatGPT Perpetuates Gender Bias in Machine Translation and Ignores Non-Gendered Pronouns: Findings across Bengali and Five other Low-Resource Languages. In AIES 2023 - Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society (pp. 901–912). Association for Computing Machinery, Inc. https://doi.org/10.1145/3600211.3604672

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