The growing societal debate and increased awareness of gender-inclusive language and non-binary identities pose new challenges for translators. While machine translation (MT) is actively being used in the translation industry, current systems lack the training data or optimization to handle gender-inclusive language strategies as they arise. MT output has been shown to exacerbate biases and perpetuate harmful stereotypes. This project aims to improve our understanding of the occurrence of bias and stereotyping in both human and machine translation by studying languages that grammatically express gender in different ways, taking into account translation directionality.