Inhalte/ Contents

Machine translation (MT) systems have become a crucial part of our daily lives – they translate texts, subtitles, and even spoken language in real time. While they help us overcome language barriers, critical questions arise: Do these systems perpetuate harmful stereotypes? Do they reinforce biases? And consequently: How can we even measure this? In this interdisciplinary research project, we are tackling these questions! Our goal is to assess an automatic evaluation pipeline that tests MT systems for gender bias. Furthermore, we will put this method into practice and evaluate different MT models - from Google Translate to DeepL to large language models like ChatGPT. Mid-Bachelor or Master students in computer science, linguistics, or social sciences with an interest in MT, gender studies, and fair technology are invited to join this project. Basic Python skills are a plus, but a willingness to learn is the most important thing. Let’s make MT fairer - together!

Fachliche:r Betreuer:in/ Supervisor

Dr.-Ing: Stefan Hillmann

Kontakt/ Contatct

michelle.kappl@tu-berlin.de

Link zum Vorlesungsverzeichnis/ Link to the course catalogue

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