Gender Bias in Italian Word Embeddings
Authors: Davide Biasion, Alessandro Fabris, Gianmaria Silvello, Gian Antonio Susto (Università di Padova, 2020)
Topic: Gender and Inclusive Language Studies

Abstract
In this work we study gender bias in Italian word embeddings (WEs), evaluating whether they encode gender stereotypes studied in social psychology or present in the labor market. We find strong associations with gender in job-related WEs. Weaker gender stereotypes are present in other domains where grammatical gender plays a significant role.

Introduction
In the literature, the study of gender bias in word embeddings (WEs) is of interest for two main reasons: (i) WEs, as components of automatic decision systems (e.g. job search tools), may contribute to harm some user groups AUTHOR; (ii) WEs can be employed as a tool to measure the biases of text corpora AUTHOR and systems for automatic text classification or information retrieval AUTHOR. In both applications, it is important to isolate the gender-related information in a subspace AUTHOR and subsequently (i) eliminate it via orthogonal projection or (ii) exploit it as a lens to study association of concepts with gender. A common taxonomy of bias in algorithms concentrates on the types of harm that they may cause AUTHOR. Allocational harms happen when a limited resource (e.g. jobs) is assigned unfairly to subgroups of a population (e.g. women and men). Representational harms arise when groups or individuals are unable to determine their image, which is presented unfavourably or neglected. Autocomplete suggestions in search engines AUTHOR are a clear example of this situation. Query completion suggestions for ``why are italian ...'' associate diverse concepts to the country and its inhabitants. Italians contribute very little to these results as they are unlikely to search information about themselves in English. Italian WEs have been developed AUTHOR and analyzed AUTHOR, following seminal work in English; analysis of gender bias has unfortunately lagged behind. Our main contribution is to close this gap, by undertaking a systematic study of gender stereotypes in Italian WEs, adapting established approaches that assess gender bias in English WEs.

Conclusion
Overall, we have analyzed gender bias in Italian WEs, adapting existing techniques and gathering data where required. We looked for stereotypical associations with gender-imbalanced professions, Career and Family, Science and Arts, finding significant associations in 2 out of 3. As expected from prior work (Gonen et al., 2019; McCurdy and Serbetci, 2020), grammatical gender is a strong confounder in these analyses. We draw the following preliminary conclusions: (i) Italian WEs seem to have less potential than their English counterparts to systematically reinforce the tested gender stereotypes, mostly due to grammatical gender. However, (ii) the influence of grammatical gender on WEs may cause different harms. As an example, in the context of job search, masculine is likely to be the default choice for queries of recruiters (male as norm - e.g. ``psicologo'' [psychologist]). Those queries would likely be closer to male candidates’ CVs than equivalent female ones, in some embedded text representations, potentially putting women at a systematic disadvantage. Both points above require further analysis of text retrieval/classification systems based on Italian WEs. Finally, (iii) isolating stereotypical concepts and gendered associations in Italian WEs along a single direction is challenging. The tested WEs show little promise as a reliable measurement tool for gender-stereotypical associations, unless combined with approaches to mitigate the influence of grammatical gender.