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Review
. 2025 May 2;6(6):101257.
doi: 10.1016/j.patter.2025.101257. eCollection 2025 Jun 13.

A decade of gender bias in machine translation

Beatrice Savoldi  1 , Jasmijn Bastings  2 , Luisa Bentivogli  1 , Eva Vanmassenhove  3
Affiliations
Review

A decade of gender bias in machine translation

Beatrice Savoldi et al. Patterns (N Y). .

Abstract

Gender bias in machine translation (MT) has been studied for over a decade, a time marked by societal, linguistic, and technological shifts. With the early optimism for a quick solution in mind, we review over 100 studies on the topic and uncover a more complex reality-one that resists a simple technical fix. While we identify key trends and advancements, persistent gaps remain. We argue that there is no simple technical solution to bias. Building on insights from our review, we examine the growing prominence of large language models and discuss the challenges and opportunities they present in the context of gender bias and translation. By doing so, we hope to inspire future work in the field to break with past limitations and to be less focused on a technical fix; more user-centric, multilingual, and multiculturally diverse; more personalized; and better grounded in real-world needs.

Keywords: automatic translation; gender; gender bias; language models; large language models; machine translation; natural language processing; neural machine translation.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Increasing interest in gender bias and machine translation The number of publications on gender and translation has generally increased over the years, showing an upward trend until 2024 both in absolute numbers (left) and relative numbers (right).
Figure 2
Figure 2
Key statistics of our literature review on gender bias in MT
See this image and copyright information in PMC

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