Gender Bias in Automatic Translation

Automatic translation tools have facilitated navigating multilingual contexts, by providing accessible shortcuts for gathering, processing, and spreading information. As language technologies become more widely used and deployed on a large scale, however, their societal impact has sparked concern both within and outside the research community. This thesis adresses gender bias affecting Machine Translation (MT) and Speech Translation (ST) models. It contributes to this pressing area of research with an interdisciplinary perspective, to raise awareness of bias, improve the understanding of the phenomenon, and investigate best practices and methods to unveil and mitigate it in translation systems.