This paper presents the DataLitMT project conducted at TH Koln – University of Applied Sciences. The project develops learning resources for teaching data literacy in its translation-specific form of professional machine translation (MT) literacy to students of translation and specialised communication programmes at BA and MA levels. We discuss the need for data literacy teaching in a translation/specialised communication context, present the three theoretical pillars of the project (consisting of a Professional MT Literacy Framework, an MT-specific data literacy framework and a competence matrix derived from these frameworks) and give an overview of the learning resources developed as part of the project.

This paper presents the project initiated by the BiasByUs team resulting from the 2021 Artificially Correct Hackaton. We briefly explain our winning participation in the hackaton, tackling the challenge on ‘Database and detection of gender bi-as in A.I. translations’, we highlight the importance of gender bias in Machine Translation (MT), and describe our pro-posed solution to the challenge, the cur-rent status of the project, and our envi-sioned future collaborations and re-search.