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DeBiasByUs : raising awareness and creating a database of MT bias

Publication type
C1
Publication status
Published
Authors
Daems, J, & Hackenbuchner, J.
Editor
Lieve Macken, Andrew Rufener, Joachim Van den Bogaert, Joke Daems, Arda Tezcan, Bram Vanroy, Margot Fonteyne, Loïc Barrault, Marta R. Costa-jussà, Ellie Kemp, Spyridon Pilos, Christophe Declercq, Maarit Koponen, Mikel L. Forcada, Carolina Scarton and Helena Moniz
Series
Proceedings of the 23rd Annual Conference of the European Association for Machine Translation
Pagination
289-290
Publisher
European Association for Machine Translation (Ghent, Belgium)
Conference
23rd Annual Conference of the European Association for Machine Translation (Ghent, Belgium)
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Abstract

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.