@inproceedings{daems-hackenbuchner-2022-debiasbyus,
    title = "{D}e{B}ias{B}y{U}s: Raising Awareness and Creating a Database of {MT} Bias",
    author = "Daems, Joke  and
      Hackenbuchner, Jani{\c{c}}a",
    editor = {Moniz, Helena  and
      Macken, Lieve  and
      Rufener, Andrew  and
      Barrault, Lo{\"i}c  and
      Costa-juss{\`a}, Marta R.  and
      Declercq, Christophe  and
      Koponen, Maarit  and
      Kemp, Ellie  and
      Pilos, Spyridon  and
      Forcada, Mikel L.  and
      Scarton, Carolina  and
      Van den Bogaert, Joachim  and
      Daems, Joke  and
      Tezcan, Arda  and
      Vanroy, Bram  and
      Fonteyne, Margot},
    booktitle = "Proceedings of the 23rd Annual Conference of the European Association for Machine Translation",
    month = jun,
    year = "2022",
    address = "Ghent, Belgium",
    publisher = "European Association for Machine Translation",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.eamt-1.34/",
    pages = "289--290",
    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 {\textquoteleft}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."
}