Gender Bias in Machine Translation and The Era of Large Language Models
This work addresses gender bias in language technologies, which is a critical issue for ensuring fairness and inclusivity in AI applications, but it is incremental as it builds on existing research.
The chapter investigates gender bias in machine translation, particularly in cross-linguistic settings, and evaluates ChatGPT's ability to address this bias in English-Italian translations, finding that ongoing advancements are needed to mitigate bias and promote fairness.
This chapter examines the role of Machine Translation in perpetuating gender bias, highlighting the challenges posed by cross-linguistic settings and statistical dependencies. A comprehensive overview of relevant existing work related to gender bias in both conventional Neural Machine Translation approaches and Generative Pretrained Transformer models employed as Machine Translation systems is provided. Through an experiment using ChatGPT (based on GPT-3.5) in an English-Italian translation context, we further assess ChatGPT's current capacity to address gender bias. The findings emphasize the ongoing need for advancements in mitigating bias in Machine Translation systems and underscore the importance of fostering fairness and inclusivity in language technologies.