Assessing Gender Bias in Machine Translation – A Case Study with Google Translate
Table 7: Percentage of female, male and neutral gender pronouns obtained for each of the merged occupation category, averaged over all occupations in said category and tested languages detailed in Table
['Category', 'Female (%)', 'Male (%)', 'Neutral (%)']
To simplify our dataset, we have decided to focus our work on job positions – which, we believe, are an interesting window into the nature of gender bias –, and were able to obtain a comprehensive list of professional occupations from the Bureau of Labor Statistics' detailed occupations table , from the United States Department of Labor. The values inside, however, had to be expanded since each line contained multiple occupations and sometimes very specific ones. Fortunately this table also provided a percentage of women participation in the jobs shown, for those that had more than 50 thousand workers. We filtered some of these because they were too generic ( "Computer occupations, all other", and others) or because they had gender specific words for the profession ("host/hostess", "waiter/waitress"). [CONTINUE] and Table 7 summarizes it even further by coalescing occupation categories into broader groups to ease interpretation.