  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.
