Automated Translation is Hopelessly Sexist, but Don’t Blame the Algorithm or the Training Data

Author: Nicolas Kayser-Bril

Publisher: Algorithm Watch

Publication Year: N/A

Summary: The following article discusses how automated translation services tend to erase women or reduce them to stereotypes. Simply tweaking the training data or the models is not enough to make translations fair. An interesting discussion of the language translation datasets and their quirks that lead to sexism in different ways and different languages. The conclusion is as follows: “Producing better machine translation probably requires input from more than just [artificial intelligence (AI)] researchers. Experts from computer science, linguistics and gender studies need to work together, Ms. Vanmassenhove said.”